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BEGIN:VEVENT
UID:ai1ec-877@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nFirm
Dynamics\nRobert Axtell\nComputational Social Science Program\,\nDepartmen
t of Computational and Data Sciences\nGeorge Mason University\, Fairfax\,
VA\n \nUp until quite recently the theory of the firm within economics was
data poor. We knew little about the empirical structure of firms outside
of narrow samples (e.g.\, manufacturing firms in some specific region\, sm
all start-up firms in some sector\, or the < 10K firms that are publicly o
wned and traded). Today we have data on all 6 million firms that have empl
oyees and the other 24 million that do not have employees. Some 120 millio
n people work for these businesses in the U.S. private sector. After revie
wing the very peculiar data on firm sizes\, ages\, growth rates\, job tenu
re\, wages paid\, and so on\, I will go on to describe a parsimonious mode
l in which 120 million software agents arrange themselves into 6 million g
roups having most of the properties of U.S. firms. Several model variation
s will be discussed. These data and models constitute the main content my
new book\, “Dynamics of Firms: Data\, Theories\, and Models\,” due out fro
m MIT Press next year.\nSeptember 14\, 2015 – 4:30 pm\nExploratory Hall\,
Room 3301\nFairfax Campus\nRefreshments will be served at 4:15 PM.
DTSTART;TZID=America/New_York:20150914T163000
DTEND;TZID=America/New_York:20150914T174000
LOCATION:3301 Exploratory Hall
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER – ROBERT A
XTELL – FIRM DYNAMICS
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n

Up until quite recently the theory of th
e firm within economics was data poor. We knew little about the empirical
structure of firms outside of narrow samples (e.g.\, manufacturing firms i
n some specific region\, small start-up firms in some sector\, or the < 10
K firms that are publicly owned and traded). Today we have data on all 6 m
illion firms that have employees and the other 24 million that do not have
employees. Some 120 million people work for these businesses in the U.S.
private sector. After reviewing the very peculiar data on firm sizes\, age
s\, growth rates\, job tenure\, wages paid\, and so on\, I will go on to d
escribe a parsimonious model in which 120 million software agents arrange
themselves into 6 million groups having most of the properties of U.S. fir
ms. Several model variations will be discussed. These data and models cons
titute the main content my new book\, “Dynamics of Firms: Data\, Theories\
, and Models\,” due out from MIT Press next year.

HTML>
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-899@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\n\nGeo
Social Media Analysis: Challenges and Opportunities\n \nArie Croitoru\nThe
Center for Geospatial Intelligence\nDepartment of Geography and Geoinform
ation Science\nGeorge Mason University\nFairfax\, VA\n\nOver the last few
years\, social media (e.g. Twitter\, Flickr\, YouTube\, etc.) have become
an integral part of the modern information and communication landscape. Th
rough social media\, individuals\, groups\, organizations\, and even state
s can now acquire\, probe\, and deliver information\, as well as shape and
reshape public opinion. At the same time\, social media content is increa
singly related to physical geographical locations. Fueled by advances in o
f Web 2.0\, mobile computing\, and spatially-aware technologies (i.e. GPS
enabled smartphones)\, social media can provide a unique opportunity to ob
serve and study the flow of information in both cyber and physical spaces.
Employing a geographically data-driven analysis approach allows us not on
ly to track how information spreads\, but also to derive information about
real-world events and happenings. We call this geospatially-driven approa
ch “GeoSocial Media Analysis.” In this presentation I will review some o
f the key trends and developments that has enabled the rise of geosocial m
edia\, and shaped their characteristics. I will then move on to describe e
xamples of various geosocial analysis methods in the context of different
application areas (e.g. natural disasters\, protests\, international relat
ions). Looking ahead\, I will conclude the presentation with some of the e
merging challenges and needs in the analysis of geosocial media data.\n\nS
eptember 21\, 2015\, 4:30 pm\n3301 Exploratory Hall\nFairfax Campus\n\nRef
reshments will be served at 4:15 PM.\n \nFind the schedule at http://www.c
masc.gmu.edu/seminars.htm\n \n
DTSTART;TZID=America/New_York:20150921T163000
DTEND;TZID=America/New_York:20150921T174000
LOCATION:3301Exploratory Hall
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – GeoSocial Media Analysis: C
hallenges and Opportunities – Arie Croitoru
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-geoso
cial-media-analysis-challenges-and-opportunities-arie-croitoru/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Over the last few years\, social media (e.g. Twitter\, Flickr\, YouTube\,
etc.) have become an integral part of the modern information and communic
ation landscape. Through social media\, individuals\, groups\, organizatio
ns\, and even states can now acquire\, probe\, and deliver information\, a
s well as shape and reshape public opinion. At the same time\, social medi
a content is increasingly related to physical geographical locations. Fuel
ed by advances in of Web 2.0\, mobile computing\, and spatially-aware tech
nologies (i.e. GPS enabled smartphones)\, social media can provide a uniqu
e opportunity to observe and study the flow of information in both cyber a
nd physical spaces. Employing a geographically data-driven analysis approa
ch allows us not only to track how information spreads\, but also to deriv
e information about real-world events and happenings. We call this geospat
ially-driven approach “GeoSocial Media Analysis.” In this presentation I
will review some of the key trends and developments that has enabled the
rise of geosocial media\, and shaped their characteristics. I will then mo
ve on to describe examples of various geosocial analysis methods in the co
ntext of different application areas (e.g. natural disasters\, protests\,
international relations). Looking ahead\, I will conclude the presentation
with some of the emerging challenges and needs in the analysis of geosoci
al media data.

\n

\n

September 21\, 2015\, 4:30 pm\n3301 Exploratory Hall\n
Fairfax Campus

\n

\n

Refreshments will be served at 4:15 PM.

\n

\n

Find the schedule at http://www.cmasc.gmu.edu/seminars.
htm

\n

\n

\n

<
!-- AddThis Share Buttons generic via filter on the_content -->
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-879@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:ESTELA BLAISTEN \; 703-993-1988\; blaisten@gmu.edu
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nExoti
c Superconductors\nAhmet Keles\nDepartment of Physics and Astronomy\nGeorg
e Mason University\nFairfax\, VA\n\nSuperconductivity has been one of the
main problems in condensed matter physics a hundred years after its discov
ery. Many results of this intensive research made it to textbooks hinting
at its fundamental importance besides promising applications. In the last
few decades\, new superconducting materials were found that were beyond th
e realm of conventional phonon based superconductors described by Bardeen-
Cooper-Schriffer (BCS) theory. This new class of superconductors found a w
ide ground in particle physics due the presence of exotic symmetries that
can be described by models akin to the Lorentz invariant field theories.\n
In this talk\, I will be focusing on a recent member of this family of exo
tic superconductors Weyl superconductors and related Majorana fermions and
discuss possible experimental realizations of these materials.\n\nSeptemb
er 28\, 2015\, 4:30 pm\nExploratory Hall\, Room 3301\nFairfax Campus\nRefr
eshments will be served at 4:15 PM.\nFind the schedule at http://www.cmasc
.gmu.edu/seminars.htm
DTSTART;TZID=America/New_York:20150928T163000
DTEND;TZID=America/New_York:20150928T174000
LOCATION:3301 EXPLORATORY HALL
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER – AHMET KE
LES – EXOTIC SUPERCONDUCTORS
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-ahmet-keles-exotic-superconductors/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Superconductivity has been one of the main problems in condensed
matter physics a hundred years after its discovery. Many results of this
intensive research made it to textbooks hinting at its fundamental importa
nce besides promising applications. In the last few decades\, new supercon
ducting materials were found that were beyond the realm of conventional ph
onon based superconductors described by Bardeen-Cooper-Schriffer (BCS) the
ory. This new class of superconductors found a wide ground in particle phy
sics due the presence of exotic symmetries that can be described by models
akin to the Lorentz invariant field theories.

\n

In this talk\, I will be focusing on a recent member of this family
of exotic superconductors Weyl superconductors and related Majorana fermio
ns and discuss possible experimental realizations of these materials.

The need for
mining massive and complex data has become paramount in areas like educati
on\, web mining\, social network analysis\, security\, and a variety of sc
ientific pursuits. However many traditional supervised and unsupervised le
arning algorithms break down when applied in big data scenarios: this is k
nown as the “big data problem”. Among other concerns\, big data presents s
erious scalability difficulties for these algorithms.

\n

In this talk
I will discuss the big data problem and solutions in the context of compl
ex data I have worked on in recent years\, namely\, social networks\, text
\, and educational data. I will also present a new method for distributed
machine learning which directly tackles key problems posing challenges to
successful and scalable mining of big data. Our method is scalable\, gener
al-purpose with regard to the machine learning algorithm\, and easily adap
table to a variety of heterogeneous grid or cloud computing scenarios.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-920@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nCo-ev
olutionary Algorithms: A Useful Computational Abstraction?\nKenneth De Jon
g\nDepartment of Computer Science and Office of Research Computing\nGeorge
Mason University\nFairfax\, VA\nOctober 19\, 2015\, 4:30 p.m.\nExplorator
y Hall\, Room 3301\n\nInterest in co‐evolutionary algorithms was triggered
in part with Hillis’ 1990 paper describing his success in using one to ev
olve sorting networks. Since then there have been heightened expectations
for using this nature‐inspired technique to improve on the range and power
of evolutionary algorithms for solving difficult computation problems. Ho
wever\, after more than two decades of exploring this promise\, the result
s have been somewhat mixed. In this talk I summarize the progress made a
nd the lessons learned with a goal of understanding how they are best used
and identify a variety of interesting open issues that need to be explore
d in order to make further progress in this area.\nRefreshments will be se
rved at 4:15 PM.\nFind the schedule at http://www.cmasc.gmu.edu/seminars.h
tm
DTSTART;TZID=America/New_York:20151019T163000
DTEND;TZID=America/New_York:20151019T174500
LOCATION:3301 Exploratory Hall\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Co-evolutionary Algorithms:
A Useful Computational Abstraction? – Kenneth De Jong
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-co-ev
olutionary-algorithms-a-useful-computational-abstraction-kenneth-de-jong/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Kenneth De Jong\nDepartment of Computer
Science and Office of Research Computing\nGeorge Mason University\nFairfax\, VA

\n

October 19\, 2015\, 4
:30 p.m.\nExploratory Hall\, Room 3301

\n

\nInterest in co‐evolutionary algorithms was triggered in part with Hi
llis’ 1990 paper describing his success in using one to evolve sorting net
works. Since then there have been heightened expectations for using this n
ature‐inspired technique to improve on the range and power of evolutionary
algorithms for solving difficult computation problems. However\, after mo
re than two decades of exploring this promise\, the results have been some
what mixed. In this talk I summarize the progress made and the lessons l
earned with a goal of understanding how they are best used and identify a
variety of interesting open issues that need to be explored in order to ma
ke further progress in this area.

The progression of Alzheimer’s disease has been linked to the aggregati
on of Aβ peptides. Current theories suggest that Aβ interactions with cell
ular membranes results in its neurodegenerative effect\, although little i
s known of the molecular mechanism. To probe this mechanism\, we performed
replica-exchange molecular dynamics simulations of Aβ peptides binding to
a model membrane represented by a lipid bilayer.

\n

In this talk I w
ill describe our investigation of the conformational change of Aβ peptides
induced by the bilayer\, the Aβ binding mechanism\, and the impact of Aβ
on bilayer structure. Our results find a correlation between the Aβ cytoto
xicity and the aggregation of Aβ peptides.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-886@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nNEW T
RANSITORY PHASES OF SILICA UNDER HIGH PRESSURE\nQingyang Hu\nGeophysical L
aboratory\nCarnegie Institution of Washington\nWashington\, D.C.\nSilica i
s one of the most-abundant natural compounds and a major component of the
Earth’s crust and mantle. Its various high-pressure forms make it an often
-used study subject for scientists interested in the transition between di
fferent chemical phases under extreme conditions\, such as those mimicking
the deep Earth. Compressing single crystal coesite SiO2 under hydrostatic
pressures of 26~53 gigapascal at room temperature\, we discover a new pol
ymorphic phase transition mechanism of coesite to post-stishovite\, by mea
ns of single-crystal synchrotron x-ray diffraction experiment and first-pr
inciples computational modeling.\nThe transition features the formation of
multiple previously unknown triclinic phases of SiO2 on the transition pa
thway as structural intermediates. Coexistence of the low-symmetry phases
results in extensive splitting of the original coesite X-ray diffraction p
eaks that appear as dramatic peak broadening and weakening\, resembling an
amorphous material. This work provides new insights into the structural t
ransition of SiO2 crystal under high pressures\, and clarifies the issue o
f the pressure-induced amorphization of coesite\, which has often been cit
ed as an archetypal example of the amorphization phenomena in general.\nNo
vember 2\, 2015 – 4:30 p.m.\nExploratory Hall\, Room 3301\nFairfax Campus
\nRefreshments will be served at 4:15 PM.\nFind the schedule at http://www
.cmasc.gmu.edu/seminars.htm\n
DTSTART;TZID=America/New_York:20151102T163000
DTEND;TZID=America/New_York:20151102T174000
LOCATION:3301 Exploratory Hall
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER – QINGYANG
HU – NEW TRANSITORY PHASES OF SILICA UNDER HIGH PRESSURE
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-qingyang-hu-new-transitory-phases-of-silica-under-high-pres
sure/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Silica is
one of the most-abundant natural compounds and a major component of the E
arth’s crust and mantle. Its various high-pressure forms make it an often-
used study subject for scientists interested in the transition between dif
ferent chemical phases under extreme conditions\, such as those mimicking
the deep Earth. Compressing single crystal coesite SiO2 under hydrostatic
pressures of 26~53 gigapascal at room temperature\, we discover a new poly
morphic phase transition mechanism of coesite to post-stishovite\, by mean
s of single-crystal synchrotron x-ray diffraction experiment and first-pri
nciples computational modeling.

\n

The transition features the format
ion of multiple previously unknown triclinic phases of SiO2 on the transit
ion pathway as structural intermediates. Coexistence of the low-symmetry p
hases results in extensive splitting of the original coesite X-ray diffrac
tion peaks that appear as dramatic peak broadening and weakening\, resembl
ing an amorphous material. This work provides new insights into the struct
ural transition of SiO2 crystal under high pressures\, and clarifies the i
ssue of the pressure-induced amorphization of coesite\, which has often be
en cited as an archetypal example of the amorphization phenomena in genera
l.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-922@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nAuger
Recombination And Its Suppression In Nanoscale Semiconductors\nRoman Vaxe
nburg\nComputational Material Science Center\nGeorge Mason University\, Fa
irfax\, VA\nand Naval Research Laboratory\, Washington\, D.C.\nNovember 9\
, 2015\, 4:30 p.m.\n Exploratory Hall\, Room 3301\nNonradiative Auger reco
mbination is an energy dissipation mechanism in which the recombination en
ergy of an electron-hole pair\, instead of being emitted as a photon\, is
transferred to another charge carrier and is eventually lost as heat. If e
fficient enough\, the Auger process can considerably reduce the radiative
efficiency of semiconductor materials\, therefore complicating their appli
cation as light emitters. As a consequence of quantum confinement\, in nan
oscale semiconductor systems such as quantum wells or quantum dots\, the A
uger recombination is greatly enhanced and is able to interfere with the r
adiative recombination. Here we present a theoretical study of the nonradi
ative Auger recombination processes in III-V quantum well light-emitting d
iodes (LEDs). The performance of these LEDs is known to steadily decrease
with increasing driving current and the origin of this efficiency droop ph
enomenon is still debatable. Our calculations show that the Auger recombin
ation can be efficient enough in these materials and that it can be the so
urce of the efficiency droop. Further\, we propose a strategy to suppress
the confinement-enhanced Auger recombination by controllable modification
of the confining potential shape. We apply this approach to both polar and
nonpolar variants of the III-V quantum well LEDs and we demonstrate an ap
preciable suppression of the Auger recombination and consequent improvemen
t of light-generation efficiency in these materials.\nRefreshments will be
served at 4:15 PM.\n———————————————————————-\nFind the schedule at http:/
/www.cmasc.gmu.edu/seminars.htm\n
DTSTART;TZID=America/New_York:20151109T163000
DTEND;TZID=America/New_York:20151109T174500
LOCATION:3301 Exploratory Hall\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Auger Recombination And Its
Suppression In Nanoscale Semiconductors – Roman Vaxenburg
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-auger
-recombination-and-its-suppression-in-nanoscale-semiconductors-roman-vaxen
burg/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Nonradiative Auger recombinati
on is an energy dissipation mechanism in which the recombination energy of
an electron-hole pair\, instead of being emitted as a photon\, is transfe
rred to another charge carrier and is eventually lost as heat. If efficien
t enough\, the Auger process can considerably reduce the radiative efficie
ncy of semiconductor materials\, therefore complicating their application
as light emitters. As a consequence of quantum confinement\, in nanoscale
semiconductor systems such as quantum wells or quantum dots\, the Auger re
combination is greatly enhanced and is able to interfere with the radiativ
e recombination. Here we present a theoretical study of the nonradiative A
uger recombination processes in III-V quantum well light-emitting diodes (
LEDs). The performance of these LEDs is known to steadily decrease with in
creasing driving current and the origin of this efficiency droop phenomeno
n is still debatable. Our calculations show that the Auger recombination c
an be efficient enough in these materials and that it can be the source of
the efficiency droop. Further\, we propose a strategy to suppress the con
finement-enhanced Auger recombination by controllable modification of the
confining potential shape. We apply this approach to both polar and nonpol
ar variants of the III-V quantum well LEDs and we demonstrate an appreciab
le suppression of the Auger recombination and consequent improvement of li
ght-generation efficiency in these materials.

\n

Refreshments will be
served at 4:15 PM.

\n

———————————————————————-

\n

Find the sche
dule at http://www.cmasc.gmu.edu/seminars.htm

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-952@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993=1988\; blaisten@gmu.edu\; http://cmasc.gm
u.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND\nT
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nInsig
hts into Cardiac Arrhythmia using a Multiscale Systems Biology Approach\nS
aleet Jafri\nDepartment of Molecular Neuroscience\nKrasnow Institute for A
dvance Studies\nGeorge Mason University\nFairfax\, VA\nNovember 16\, 2015
– 4:30 pm\nExploratory Hall\, Room 3301\nFairfax Campus\nHeart disease is
the leading cause of death in the developed world and is being recognized
as and increasing problem in the the developing world. A fatal cardiac arr
hythmia is often the underlying cause of death. One class of arrhythmia ri
ses from defects in calcium dynamics in the heart. Calcium plays a major r
ole on heart function linking the electrical depolarization of the heart w
ith contraction. We use an integrative multiscale model to explore how def
ects on the level of molecular function can lead to arrhythmia in the hear
t.\nThese simulations cross many orders of magnitude on the spatial and te
mporal scales to provide insight into mechanisms of cardiac arrhythmia. To
make this problem tractable we have employed novel computational algorith
ms and leverage modern GPU computing techniques. The insights gained by th
ese studies provide advanced our understanding of arrhythmia such as how a
n arrhythmia is initiated\, how genetic defects can contribute to arrhythm
ia\, and how do changes to structure and gene expression during disease in
crease the likelihood of arrhythmia.\nRefreshments will be served at 4:15
PM.\n———————————————————————-\nFind the schedule at http://www.cmasc.gmu.e
du/seminars.htm\n
DTSTART;TZID=America/New_York:20151116T163000
DTEND;TZID=America/New_York:20151116T174500
LOCATION:3301 Exploratory Hall\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Insights into Cardiac Arrhyt
hmia using a Multiscale Systems Biology Approach – Saleet Jafri
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-insig
hts-into-cardiac-arrhythmia-using-a-multiscale-systems-biology-approach-sa
leet-j/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Heart disease is the leading cause of death in
the developed world and is being recognized as and increasing problem in
the the developing world. A fatal cardiac arrhythmia is often the underlyi
ng cause of death. One class of arrhythmia rises from defects in calcium d
ynamics in the heart. Calcium plays a major role on heart function linking
the electrical depolarization of the heart with contraction. We use an in
tegrative multiscale model to explore how defects on the level of molecula
r function can lead to arrhythmia in the heart.

\n

These simulations
cross many orders of magnitude on the spatial and temporal scales to provi
de insight into mechanisms of cardiac arrhythmia. To make this problem tra
ctable we have employed novel computational algorithms and leverage modern
GPU computing techniques. The insights gained by these studies provide ad
vanced our understanding of arrhythmia such as how an arrhythmia is initia
ted\, how genetic defects can contribute to arrhythmia\, and how do change
s to structure and gene expression during disease increase the likelihood
of arrhythmia.

\n

Refreshments will be served at 4:15 PM.

\n

———
————————————————————-

\n

Find the schedule at http://www.cmasc.gmu.ed
u/seminars.htm

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-887@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL & DATA SCIENCES\n(CSI 898-Sec 001)\nDETERMI
NING OPTIMAL POLICE PATROL AREAS WITH MAXIMAL COVERING AND BACKUP COVERING
LOCATION MODELS\nKevin Curtin\nDepartment of Geography and Geoinformation
Science and\,\nDepartment of Computational and Data Sciences\nGeorge Maso
n University\,\nFairfax\, VA\nIn this talk I will present a new method for
determining efficient spatial distributions of police patrols in a metrop
olitan region. A mathematical model called the police patrol area covering
(PPAC) model is formulated and solved. The method uses inputs of police g
eography (divisions\, sectors\, beats\, and response areas) within a geogr
aphic information systems (GIS) framework\, analyzes that data through an
optimal covering model formulation\, and provides alternative optimal solu
tions for presentation to decision makers. The goals of this research are
to increase the level of police service in the face of variation in both t
he demand for police services and the availability of police resources. Ex
amples of the inputs from – and outputs to – GIS are provided through a pi
lot study of the city of Dallas\, Texas.\nNovember 30\, 2015 – 4:30 p.m.\n
Exploratory Hall\, Room 3301\nFairfax Campus\nRefreshments will be served
at 4:15 PM.\nFind the schedule at http://www.cmasc.gmu.edu/seminars.htm\n
DTSTART;TZID=America/New_York:20151130T163000
DTEND;TZID=America/New_York:20151130T174000
LOCATION:3301 Exploratory Hall
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER – KEVIN CU
RTIN – DETERMINING OPTIMAL POLICE PATROL AREAS WITH MAXIMAL COVERING AND B
ACKUP COVERING LOCATION MODELS
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-kevin-curtin-determining-optimal-police-patrol-areas-with-m
aximal-covering-and-backup-covering-location-models/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

In this talk I will present a new metho
d for determining efficient spatial distributions of police patrols in a m
etropolitan region. A mathematical model called the police patrol area cov
ering (PPAC) model is formulated and solved. The method uses inputs of pol
ice geography (divisions\, sectors\, beats\, and response areas) within a
geographic information systems (GIS) framework\, analyzes that data throug
h an optimal covering model formulation\, and provides alternative optimal
solutions for presentation to decision makers. The goals of this research
are to increase the level of police service in the face of variation in b
oth the demand for police services and the availability of police resource
s. Examples of the inputs from – and outputs to – GIS are provided through
a pilot study of the city of Dallas\, Texas.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-6117@cos.gmu.edu
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:
DESCRIPTION:Dissertation Defense\nCandidate: Robert Reznik\nTitle: Electric
Field Effects On Single And Small Networks of Neurons\nAbstract:\nAn elec
tric field can polarize a neuron\, especially a neuron with elongated dend
rites\, and thus modify its excitability. In this dissertation\, we use co
mputational models\, experimental data\, and analysis to investigate the e
ffects on neural excitability due to externally applied static electric fi
elds and the extracellular currents generated by a spiking neuron. We appl
y our results to individual and small networks of neurons. This work has p
otential implications in the areas of neural prosthetics and therapies to
treat a number of neurological conditions such as Depression and Epilepsy.
This work also contributes to our understanding of the effects of electri
c fields endogenous to the brain. We first address the effects of polariza
tion induced by non-weak electric fields on elongated neurons with active
dendrites. We find that our model agrees with experimental observation for
both weak and strong polarization. For intermediate polarization\, we ide
ntify novel behavior that should be amenable to experimental verification.
\nThrough analysis and modeling\, we determine the underlying mechanisms f
or the observed behavior. Currents that are negligible at weak polarizatio
ns and associated with the spiking or bursting phase of a neuron play an i
mportant role in the response of a resting neuron to either injected or sy
naptically generated stimulus. For weak polarizations\, small differences
in model parameters such as extracellular potassium and the rate of curren
t injection yield proportionally small differences in the trajectory of th
e state variables of the neuron. However\, as polarization strength increa
ses the trajectories begin to diverge falling into either a sublinear or s
uperlinear response categories with respect to polarization.\nWe identify
the relative strengths of the hyperpolarizing and depolarizing active dend
rite currents as a predictor of polarization-dependent excitability. In th
e second part of the dissertation we look at localized ephaptic effects du
e to a single spiking neuron on spike propagation in a chain of synaptical
ly connected neurons. Modeling the extracellular currents using a resistiv
e lattice we observe a non-monotonic relationship between excitability and
extracellular resistance. Furthermore\, this surprising result is evident
for a range of resistances that fall within those estimated using experim
entally measured parameters. We define three mechanisms of the localized e
phaptic effects\; source loading\, synaptic coupling\, and non-synaptic me
mbrane currents. Through computational experiment and analysis\, we are ab
le to analyze these effects as a function of the time between the ephaptic
polarization and synaptic input as well as extracellular resistance.\nDir
ector or Committee Chair:\nDr. Evelyn Sander\nCommittee:\nDr. Evelyn Sande
rDr. Ernest BarretoDr. Nathalia PeixotoDr. Juan Cebral\n\nNotes: The thesi
s is on reserve in the Johnson Center Library\, Fairfax Campus. All member
s of the George Mason University community are invited to attend.
DTSTART;TZID=America/New_York:20160120T130000
SEQUENCE:0
SUMMARY:PhD Dissertation Defense: Robert Reznik
URL:http://cos.gmu.edu/cds/event/phd-dissertation-defense-robert-reznik/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

\n

Dissertation Defense

\nCandidate: Robert Reznik\nTitle: Electric Field Effects On Single
And Small Networks of Neurons\n

Abstract:

\n<
p>An electric field can polarize a neuron\, especially a neuron with elong
ated dendrites\, and thus modify its excitability. In this dissertation\,
we use computational models\, experimental data\, and analysis to investig
ate the effects on neural excitability due to externally applied static el
ectric fields and the extracellular currents generated by a spiking neuron
. We apply our results to individual and small networks of neurons. This w
ork has potential implications in the areas of neural prosthetics and ther
apies to treat a number of neurological conditions such as Depression and
Epilepsy. This work also contributes to our understanding of the effects o
f electric fields endogenous to the brain. We first address the effects of
polarization induced by non-weak electric fields on elongated neurons wit
h active dendrites. We find that our model agrees with experimental observ
ation for both weak and strong polarization. For intermediate polarization
\, we identify novel behavior that should be amenable to experimental veri
fication.\n

Through analysis and modeling\, we determine the underly
ing mechanisms for the observed behavior. Currents that are negligible at
weak polarizations and associated with the spiking or bursting phase of a
neuron play an important role in the response of a resting neuron to eithe
r injected or synaptically generated stimulus. For weak polarizations\, sm
all differences in model parameters such as extracellular potassium and th
e rate of current injection yield proportionally small differences in the
trajectory of the state variables of the neuron. However\, as polarization
strength increases the trajectories begin to diverge falling into either
a sublinear or superlinear response categories with respect to polarizatio
n.

\n

We identify the relative strengths of the hyperpolarizing and d
epolarizing active dendrite currents as a predictor of polarization-depend
ent excitability. In the second part of the dissertation we look at locali
zed ephaptic effects due to a single spiking neuron on spike propagation i
n a chain of synaptically connected neurons. Modeling the extracellular cu
rrents using a resistive lattice we observe a non-monotonic relationship b
etween excitability and extracellular resistance. Furthermore\, this surpr
ising result is evident for a range of resistances that fall within those
estimated using experimentally measured parameters. We define three mechan
isms of the localized ephaptic effects\; source loading\, synaptic couplin
g\, and non-synaptic membrane currents. Through computational experiment a
nd analysis\, we are able to analyze these effects as a function of the ti
me between the ephaptic polarization and synaptic input as well as extrace
llular resistance.

Director or Committee Chair:<
/h4>\n

Committee:

Notes: The thesis is on reser
ve in the Johnson Center Library\, Fairfax Campus. All members of the Geor
ge Mason University community are invited to attend.

\n

\n

\n

\n
X-INSTANT-EVENT:1
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1234@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION: \nCOLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND
\nTHE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nNe
utron Scattering from Nanostructured Oxide Materials
\nKrishnamurthy V. Vemuru\nDepartment of Physics and Astronomy\nGeorge Ma
son University\nFairfax\, VA\nFebruary 8\, 2016 – 4:30 pm\nExploratory Hal
l\, Room 3301\nFairfax Campus\nNeutrons can penetrate deeply into material
s. They can sense the position of atomic nuclei through strong interaction
and magnetic moments through dipole-dipole interaction. The scattering of
neutrons by both these mechanisms is well understood\, so theories need n
ot be concerned about the modeling or simulating the scattering process. I
nstead\, the investigations can focus on the computation and experimental
determination of the intrinsic positions of nuclei and spin-correlation fu
nctions. This enables neutron scattering as a unique tool to determine cry
stal structures\, nanostructures\, spin dynamics and phonon dispersions in
solids. In this talk\, I will focus on the applications of small angle
neutron scattering to determine the orientational ordering of nanostructur
es in MgO nanorods\, TiO2 nanowires and gamma-Fe2O3 nanoparticle dispersio
ns. I will also discuss how small neutron angle scattering from nanostru
ctures is modeled and calculated.\nRefreshments will be served at 4:15 PM.
\n———————————————————————-\nFind the schedule at http://www.cmasc.gmu.edu/
seminars.htm\n \n
DTSTART;TZID=America/New_York:20160208T163000
DTEND;TZID=America/New_York:20160208T174500
LOCATION:3301 Exploratory Hall\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Neutron Scattering from Nano
structured Oxide Materials – Krishnamurthy V. Vemuru
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-neutr
on-scattering-from-nanostructured-oxide-materials-krishnamurthy-v-vemuru/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Neutrons can penetrate deeply into materials. They can sense the position
of atomic nuclei through strong interaction and magnetic moments through
dipole-dipole interaction. The scattering of neutrons by both these mechan
isms is well understood\, so theories need not be concerned about the mode
ling or simulating the scattering process. Instead\, the investigations ca
n focus on the computation and experimental determination of the intrinsic
positions of nuclei and spin-correlation functions. This enables neutron
scattering as a unique tool to determine crystal structures\, nanostructur
es\, spin dynamics and phonon dispersions in solids. In this talk\, I wi
ll focus on the applications of small angle neutron scattering to determin
e the orientational ordering of nanostructures in MgO nanorods\, TiO2 nano
wires and gamma-Fe2O3 nanoparticle dispersions. I will also discuss how
small neutron angle scattering from nanostructures is modeled and calculat
ed.

\n

Refreshments will be served at 4:15 PM.

\n

——————————————
—————————-

\n

Find the schedule at http://www.cmasc.gmu.edu/seminars.
htm

\n

\n

\n

<
!-- AddThis Share Buttons generic via filter on the_content -->
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1261@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)\nHigh L
evel Information Fusion for Crisis Response Planning\nKathryn Blackmond La
skey\nDepartment of Systems Engineering and Operations Research\nGeorge Ma
son University\nFairfax\, VA\nFebruary 29\, 2016\, 4:30 pm\nExploratory Ha
ll\, Room 3301\nFairfax Campus\nEach year\, natural and anthropogenic cris
es disrupt the lives of millions of people. Local\, national and internati
onal crisis response systems struggle to cope with urgent needs during and
immediately after a crisis. The challenges multiply as population grows\,
density of urban areas increases\, and coastal areas become more vulnerab
le to rising sea levels. A typical crisis scenario requires coordinating m
any diverse players\, including local\, national and international militar
y\, other governmental\, and non-governmental organizations. Often\, no si
ngle entity is in charge of the response\, making coordination even more d
ifficult. There is an urgent need for better ways to allocate resources\,
maintain situation awareness\, and reallocate resources as the situation c
hanges. Multi-source information fusion is vital to effective resource all
ocation and situation awareness. Some of the greatest inefficiencies in
crisis response stem from the inability to exchange information between sy
stems designed for different purposes and operating under different owners
hip. Too often\, information integration and fusion must be performed manu
ally. Greater automation could support more timely\, better coordinated re
sponses in situations where time is of the essence. The greatest need is n
ot for low-level fusion of sensor reports to classify and track individual
objects\, but for high-level fusion to characterize complex situations an
d support planning of effective responses. This talk describes challenge
s of high-level fusion for crisis management\, proposes a technical framew
ork for addressing high-level fusion\, and discusses how effectively addre
ssing high-level fusion challenges can improve efficiency of crisis respon
se. We illustrate our ideas with a case study involving a humanitarian rel
ief operation in a flooding scenario.\nRefreshments will be served at 4:15
PM.\n———————————————————————-\nFind the schedule at http://www.cmasc.gmu.
edu/seminars.htm
DTSTART;TZID=America/New_York:20160229T163000
DTEND;TZID=America/New_York:20160229T174500
LOCATION:Exploratory Hall\, Room 3301
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – High Level Information Fusio
n for Crisis Response Planning – Kathryn Blackmond Laskey
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-high-
level-information-fusion-for-crisis-response-planning-kathryn-blackmond-la
skey/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Each year\,
natural and anthropogenic crises disrupt the lives of millions of people.
Local\, national and international crisis response systems struggle to co
pe with urgent needs during and immediately after a crisis. The challenges
multiply as population grows\, density of urban areas increases\, and coa
stal areas become more vulnerable to rising sea levels. A typical crisis s
cenario requires coordinating many diverse players\, including local\, nat
ional and international military\, other governmental\, and non-government
al organizations. Often\, no single entity is in charge of the response\,
making coordination even more difficult. There is an urgent need for bette
r ways to allocate resources\, maintain situation awareness\, and realloca
te resources as the situation changes. Multi-source information fusion is
vital to effective resource allocation and situation awareness. Some of
the greatest inefficiencies in crisis response stem from the inability to
exchange information between systems designed for different purposes and o
perating under different ownership. Too often\, information integration an
d fusion must be performed manually. Greater automation could support more
timely\, better coordinated responses in situations where time is of the
essence. The greatest need is not for low-level fusion of sensor reports t
o classify and track individual objects\, but for high-level fusion to cha
racterize complex situations and support planning of effective responses.
This talk describes challenges of high-level fusion for crisis managemen
t\, proposes a technical framework for addressing high-level fusion\, and
discusses how effectively addressing high-level fusion challenges can impr
ove efficiency of crisis response. We illustrate our ideas with a case stu
dy involving a humanitarian relief operation in a flooding scenario.

\n

Refreshments will be served at 4:15 PM.

\n

———————————————————————
-\nFind the schedule at http://www.cmasc.gmu.edu/seminars.htm

\n<
!-- AddThis Advanced Settings above via filter on the_content -->

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1236@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL & DATA SCIENCES\n(CSI 898-Sec 001)\nPulse I
mage Processing via Image Operators\nJason M. Kinser\nDepartment of Comput
ational and Data Sciences\n& Department of Physics and Astronomy\nGeorge M
ason University\nFairfax\, VA\nMarch 14\, 2016\, 4:30 pm\nExploratory Hall
\, Room 3301\nFairfax Campus\nThis talk will present two related topics. T
he first is a discussion of image operators\, which is a developing mathem
atical notation that coalesces modern computer scripting with precise desc
riptions of image processing algorithms. The second is an exploration of p
ulse image processing which is a tool set based on discoveries of the mamm
alian visual cortex. These processes are capable of extracting coupled sha
pes and textures from images that are the foundations for many analysis al
gorithms. The inherent nature of these algorithms are capable of retrievin
g traditional features as well as image signatures which are invariant to
position\, rotation and limited scale. Several applications of pulse image
processing will be reviewed to demonstrate the efficacy of this approach.
\nRefreshments will be served at 4:15 PM.\n———————————————————————-\nFind
the schedule at http://www.cmasc.gmu.edu/seminars.htm\n
DTSTART;TZID=America/New_York:20160314T163000
DTEND;TZID=America/New_York:20160314T174500
LOCATION:3301 Exploratory Hall\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Pulse Image Processing via I
mage Operators – Jason Kinser
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-pulse
-image-processing-via-image-operators-jason-kinser/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

This talk will present two related topics. The first is a discuss
ion of image operators\, which is a developing mathematical notation that
coalesces modern computer scripting with precise descriptions of image pro
cessing algorithms. The second is an exploration of pulse image processing
which is a tool set based on discoveries of the mammalian visual cortex.
These processes are capable of extracting coupled shapes and textures from
images that are the foundations for many analysis algorithms. The inheren
t nature of these algorithms are capable of retrieving traditional feature
s as well as image signatures which are invariant to position\, rotation a
nd limited scale. Several applications of pulse image processing will be r
eviewed to demonstrate the efficacy of this approach.

\n

Refreshments
will be served at 4:15 PM.\n———————————————————————-\nFind th
e schedule at http://www.cmasc.gmu.edu/seminars.htm

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1303@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nSpect
rum Sensing in Time\, Space\, and Frequency\nBrian L. Mark\nDepartment of
Electrical & Computer Engineering\nGeorge Mason University\nFairfax\, VA\n
Wireless spectrum is becoming a scarce resource largely due to the static
allocation of licensed spectrum by regulatory bodies\, resulting in a high
degree of underutilization in large portions of the spectrum. One approac
h to alleviating spectrum scarcity involves the use of cognitive radios th
at are capable of sensing the wireless environment and dynamically accessi
ng unused portions of the spectrum\, while avoiding harmful interference t
o licensed users of a spectrum band. Dynamic spectrum access is promising\
, but there remain technological challenges in spectrum sensing and resour
ce allocation\, as well as spectrum policy issues\, which need to be addre
ssed for it to be widely deployed. In this talk\, we will discuss mode
ls and algorithms for performing spectrum sensing in time\, space\, and fr
equency. Spectrum sensing essentially amounts to characterizing primary us
ers in terms of temporal dynamics\, spatial location and power usage\, and
center frequency and bandwidth usage. Such characterization must also tak
e into account wireless propagation effects and impairments. We will discu
ss the application of a class of hidden Markov models to characterizing th
e temporal dynamics of primary users\, as well as their spatial and freque
ncy domain properties.\n\nMarch 21\, 2016 – 4:30 p.m.\nExploratory Hall\,
Room 3301\nFairfax Campus\nRefreshments will be served at 4:15 PM.\n——————
——————————————————————————————————————————————————————————–\nFind the sche
dule at http://www.cmasc.gmu.edu/seminars.htm\n
DTSTART;TZID=America/New_York:20160321T163000
DTEND;TZID=America/New_York:20160321T174500
GEO:+38.833737;-77.32653
LOCATION:Exploratory Hall\, Room 3301 @ Campus Dr\, Fairfax\, VA 22030\, US
A
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Spectrum Sensing in Time\, S
pace\, and Frequency – Brian Mark
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-spect
rum-sensing-in-time-space-and-frequency-brian-mark/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Wireless spectrum is becoming a scarce resource largely
due to the static allocation of licensed spectrum by regulatory bodies\, r
esulting in a high degree of underutilization in large portions of the spe
ctrum. One approach to alleviating spectrum scarcity involves the use of c
ognitive radios that are capable of sensing the wireless environment and d
ynamically accessing unused portions of the spectrum\, while avoiding harm
ful interference to licensed users of a spectrum band. Dynamic spectrum ac
cess is promising\, but there remain technological challenges in spectrum
sensing and resource allocation\, as well as spectrum policy issues\, whic
h need to be addressed for it to be widely deployed. In this talk\, we
will discuss models and algorithms for performing spectrum sensing in tim
e\, space\, and frequency. Spectrum sensing essentially amounts to charact
erizing primary users in terms of temporal dynamics\, spatial location and
power usage\, and center frequency and bandwidth usage. Such characteriza
tion must also take into account wireless propagation effects and impairme
nts. We will discuss the application of a class of hidden Markov models to
characterizing the temporal dynamics of primary users\, as well as their
spatial and frequency domain properties.

The solidificatio
n of binary and multicomponent alloys occurs in many industrial and geophy
sical systems. Often in these systems instabilities of the solid/liquid in
terface lead to dendritic regions that separate completely solid regions f
rom the liquid region. These so-called mushy layers have important consequ
ences for heat and solute transfer between the solid and liquid regions.
In this talk we outline mathematical models and their connection to labor
atory experiments that help understand the physical processes occurring in
these systems. Novel fluid dynamical phenomena that occur in mushy layers
will be discussed for binary and ternary systems.

\n

March 28\, 2016\, 4:30 pm\nExploratory Hall\, Room 330l<
/p>\n

\n

Refreshments will be served at 4:15 PM.

\n

————————
————————————————————————————————————————————————————————–

\n

Find the
schedule at http://www.cmasc.gmu.edu/seminars.htm

\n

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1304@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL & DATA SCIENCES\n(CSI 898-Sec 001)\nMediato
rs of Memory and Learning in the Brain\nNathalia Peixoto\nDepartment of El
ectrical & Computer Engineering\nGeorge Mason University\nFairfax\, VA\nIn
this talk we will describe two projects in the neural engineering lab\, b
oth focusing on neural interfaces. One project is in collaboration with th
e FDA and aims at demonstrating the safety in retinal implant devices. We
will discuss how we model an implantable electrode in terms of electric ci
rcuits. The second project focuses on neural cell cultures and we will pre
sent results on how to interface with networks over time and train them to
respond to electrical stimuli. We intend to discuss how that impacts our
understanding of the mechanisms mediating memory and learning in the brain
. Some of the other projects in the lab are described in our site: http:
//neural.bioengineering.gmu.edu.\nApril 4\, 2016-4:30 pm\nExploratory Hall
\, Room 3301\nFairfax Campus\nRefreshments will be served at 4:15 PM.\n———
—————————————————————————————————————————————————————————————–\nFind the s
chedule at http://www.cmasc.gmu.edu/seminars.htm\n
DTSTART;TZID=America/New_York:20160404T163000
DTEND;TZID=America/New_York:20160404T171500
GEO:+38.833737;-77.32653
LOCATION:Exploratory Hall\, Room 3301 @ Campus Dr\, Fairfax\, VA 22030\, US
A
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Mediators of Memory and Lear
ning in the Brain – Nathalia Peixoto
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-media
tors-of-memory-and-learning-in-the-brain-nathalia-peixoto/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

In this talk we will describe two projects in the neural e
ngineering lab\, both focusing on neural interfaces. One project is in col
laboration with the FDA and aims at demonstrating the safety in retinal im
plant devices. We will discuss how we model an implantable electrode in te
rms of electric circuits. The second project focuses on neural cell cultur
es and we will present results on how to interface with networks over time
and train them to respond to electrical stimuli. We intend to discuss how
that impacts our understanding of the mechanisms mediating memory and lea
rning in the brain. Some of the other projects in the lab are described
in our site: http://neural.bioengineering.gmu.edu.

\n

April 4\, 2016-4:30 pm\nExploratory Hall\, Room 3301\nFairfax Campus

\n

Refreshments will be served at 4:15 PM.

\n

————————————————————————————————————————————————————————————————–

\n

Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1260@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL & DATA SCIENCES (CSI 898-Sec 001)\nMechanic
al Effects of Fluids Adsorption by Nanoporous Materials\nGennady Gor\nCent
er for Computational Materials Science\nNaval Research Laboratory\nWashing
ton\, DC\nApril 11\, 2016 4:30 pm\nExploratory Hall\, Room 3301\nFairfax C
ampus\nIn this talk I will focus on two mechanical effects related to flui
d adsorption by porous materials: adsorption-induced deformation and chang
e of elastic properties of fluids during adsorption. The development of qu
antitative theories of these effects will provide the opportunity to emplo
y them for new sensing technologies and new approaches for the characteriz
ation of nanoporous materials. Adsorption-induced deformation is a change
of shape or volume of a porous sample during adsorption. It could be eithe
r expansion or contraction\, and while the former is well understood\, the
latter is still puzzling due to the apparent contradiction with the Gibbs
adsorption equation. I will show how this apparent contradiction can be r
esolved by relating the strain of the solid to the change of the surface s
tress due to adsorption. I will present the results of the surface stresse
s calculations based on ab initio molecular dynamics simulations. In the s
econd part of the talk I will focus on the effects of nanoconfinement on t
he mechanical properties of the adsorbed fluid. Recent ultrasonic experime
nts have shown that the elastic modulus of argon adsorbed in nanoporous gl
ass noticeably differs from the elastic modulus of bulk liquid argon. I wi
ll present the results of a molecular modeling study\, which explains thes
e experimental observations and sheds light on possibilities of ultrasonic
investigation of nanoporous materials.\n\nRefreshments will be served at
4:15 PM.\nFind the schedule at http://www.cmasc.gmu.edu/seminars.htm
DTSTART;TZID=America/New_York:20160411T163000
DTEND;TZID=America/New_York:20160411T174500
LOCATION:Exploratory Hall\, Room 3301
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL & DATA SCIENCES – Mechanical Effects of Fluids
Adsorption by Nanoporous Materials – Gennady Gor
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-data-sciences-mechanica
l-effects-of-fluids-adsorption-by-nanoporous-materials-gennady-gor/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

In this t
alk I will focus on two mechanical effects related to fluid adsorption by
porous materials: adsorption-induced deformation and change of elastic pro
perties of fluids during adsorption. The development of quantitative theor
ies of these effects will provide the opportunity to employ them for new s
ensing technologies and new approaches for the characterization of nanopor
ous materials. Adsorption-induced deformation is a change of shape or volu
me of a porous sample during adsorption. It could be either expansion or c
ontraction\, and while the former is well understood\, the latter is still
puzzling due to the apparent contradiction with the Gibbs adsorption equa
tion. I will show how this apparent contradiction can be resolved by relat
ing the strain of the solid to the change of the surface stress due to ads
orption. I will present the results of the surface stresses calculations b
ased on ab initio molecular dynamics simulations. In the second part of th
e talk I will focus on the effects of nanoconfinement on the mechanical pr
operties of the adsorbed fluid. Recent ultrasonic experiments have shown t
hat the elastic modulus of argon adsorbed in nanoporous glass noticeably d
iffers from the elastic modulus of bulk liquid argon. I will present the r
esults of a molecular modeling study\, which explains these experimental o
bservations and sheds light on possibilities of ultrasonic investigation o
f nanoporous materials.

\n

\n

Refreshments will be served at 4:15 PM.

\n

Find the schedule at http://www.cmasc.gmu.edu/seminars.htm
p>\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1235@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND\nT
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nEcono
mic inequality from statistical physics point of view \nVictor Yakoven
ko\nDepartment of Physics\nUniversity of Maryland\nCollege Park\, MD\nApri
l 18\, 2016 – 4:30 pm\nExploratory Hall\, Room 3301\nFairfax Campus\nSimil
arly to the probability distribution of energy in physics\, the probabilit
y distribution of money among the agents in a closed economic system is al
so expected to follow the exponential Boltzmann-Gibbs law\, as a consequen
ce of entropy maximization. Analysis of empirical data shows that income d
istributions in the USA\, European Union\, and other countries exhibit a w
ell-defined two-class structure. The majority of the population (about 97%
) belongs to the lower class characterized by the exponential (“thermal”)
distribution. The upper class (about 3% of the population) is characterize
d by the Pareto power-law (“superthermal”) distribution\, and its share of
the total income expands and contracts dramatically during booms and bust
s in financial markets. Globally\, data analysis of energy consumption p
er capita around the world shows decreasing inequality in the last 30 year
s and convergence toward the exponential probability distribution\, in agr
eement with the maximal entropy principle. Similar results are found for t
he global probability distribution of CO2 emissions per capita. All papers
are available at http://physics.umd.edu/~yakovenk/econophysics/. For rece
nt coverage in Science magazine\, see http://www.sciencemag.org/content/34
4/6186/828\nRefreshments will be served at 4:15 PM.\n—————————————————————
——-\nFind the schedule at http://www.cmasc.gmu.edu/seminars.htm
DTSTART;TZID=America/New_York:20160418T163000
DTEND;TZID=America/New_York:20160418T174500
LOCATION:3301 Exploratory Hall\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Economic inequality from sta
tistical physics point of view – Victor Yakovenko
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-econo
mic-inequality-from-statistical-physics-point-of-view-victor-yakovenko/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Similarly to the probability distributio
n of energy in physics\, the probability distribution of money among the a
gents in a closed economic system is also expected to follow the exponenti
al Boltzmann-Gibbs law\, as a consequence of entropy maximization. Analysi
s of empirical data shows that income distributions in the USA\, European
Union\, and other countries exhibit a well-defined two-class structure. Th
e majority of the population (about 97%) belongs to the lower class charac
terized by the exponential (“thermal”) distribution. The upper class (abou
t 3% of the population) is characterized by the Pareto power-law (“superth
ermal”) distribution\, and its share of the total income expands and contr
acts dramatically during booms and busts in financial markets. Globally\
, data analysis of energy consumption per capita around the world shows de
creasing inequality in the last 30 years and convergence toward the expone
ntial probability distribution\, in agreement with the maximal entropy pri
nciple. Similar results are found for the global probability distribution
of CO2 emissions per capita. All papers are available at http://physics.um
d.edu/~yakovenk/econophysics/. For recent coverage in Science magazine\, s
ee http://www.sciencemag.org/content/344/6186/828

\n

Refreshments wil
l be served at 4:15 PM.

\n

———————————————————————-

\n

Find the
schedule at http://www.cmasc.gmu.edu/seminars.htm

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1331@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaistein\; 703-993-1988\; blaisten@gmu.edu\; http://www.cma
sc.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)\nTOWARD
S RELIABLE SPATIAL AND SPATIO-TEMPORAL PATTERN ANALYSIS\n \nMatthias Renz
\nDepartment of Computational and Data Sciences\nGeorge Mason University\n
Fairfax\, VA\n \nCurrent technology trends\, such as smart phones\, mobile
devices\, stationary sensors and satellites\, coupled with a new user men
tality of voluntarily sharing information generates a huge volume of geo-s
patial and geo-temporal data that might be useful for many applications. I
ndeed\, the increasing volume of geo-spatial data from heterogeneous sourc
es is an example of Big Data. In this talk I will present effective and ef
ficient solutions to problems related to reliable spatial pattern analysis
and mining of uncertain spatial and spatio-temporal data. These technique
s can substantially improve the quality of decision making\, minimize risk
\, and unearth valuable insights from data that would otherwise remain hid
den. Use of uncertain data presents a two-fold challenge: 1) identifying p
otential solutions and assigning a probability to each solution such that
the user is confident about the results\, and 2) enabling fast computation
s such that the user obtains results in a reasonable time\, even for large
data sets.\nApril 25\, 2016\, 4:30 pm\nExploratory Hall\, Room 3301\nFair
fax Campus\n \nRefreshments will be served at 4:15 PM.\n——————————————————
—————-\nFind the schedule at http://www.cmasc.gmu.edu/seminars.htm\n \n
DTSTART;TZID=America/New_York:20160425T163000
DTEND;TZID=America/New_York:20160425T174500
GEO:+38.846224;-77.306373
LOCATION:Exploratory Hall\, Room 3301 @ Fairfax\, VA\, USA
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Towards Reliable Spatial And
Spatio-Temporal Pattern Analysis – Matthias Renz
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-towar
ds-reliable-spatial-and-spatio-temporal-pattern-analysis-matthias-renz/
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Current technology trends\, such as smart phones\, mobile
devices\, stationary sensors and satellites\, coupled with a new user men
tality of voluntarily sharing information generates a huge volume of geo-s
patial and geo-temporal data that might be useful for many applications. I
ndeed\, the increasing volume of geo-spatial data from heterogeneous sourc
es is an example of Big Data. In this talk I will present effective and ef
ficient solutions to problems related to reliable spatial pattern analysis
and mining of uncertain spatial and spatio-temporal data. These technique
s can substantially improve the quality of decision making\, minimize risk
\, and unearth valuable insights from data that would otherwise remain hid
den. Use of uncertain data presents a two-fold challenge: 1) identifying p
otential solutions and assigning a probability to each solution such that
the user is confident about the results\, and 2) enabling fast computation
s such that the user obtains results in a reasonable time\, even for large
data sets.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2452@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:KAREN UNDERWOOD\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/calendar/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nFriday\, September
2\, 3:00-4:30 p.m.\nCenter for Social Complexity Suite\n3rd Floor Researc
h Hall\, Fairfax Campus\nMatthew Oldham\, CSS PhD student\nGeorge Mason Un
iversity\nTo big wing\, or not to big wing\, now an answer\nThe Churchilli
an quote “Never\, in the field of human conflict\, was so much owed by so
many to so few”\, encapsulates perfectly the heroics of Royal Air Force (R
AF) Fighter Command (FC) during the Battle of Britain. Despite the undoubt
ed heroics\, questions remain about how FC employed the ‘so few’. In parti
cular\, the question as to whether FC should have employed the ‘Big Wing’
tactics\, as per 12 Group\, or implement the smaller wings as per 11 Group
\, remains a source of much debate. In this paper\, I create an agent base
d model (ABM) simulation of the Battle of Britain\, which provides valuabl
e in- sight into the key components that influenced the loss rates of both
sides. It provides mixed support for the tactics employed by 11 Group\, a
s the model identified numerous variables that impacted the success or oth
erwise of the British.\nLight refreshments will be served.
DTSTART;TZID=America/New_York:20160902T150000
DTEND;TZID=America/New_York:20160902T163000
LOCATION:CENTER FOR SOCIAL COMPLEXITY @ 3RD FLOOR\, RESEARCH HALL\, FAIRFAX
CAMPUS
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – SEPTEMBER 2 – OLDHAM
URL:http://cos.gmu.edu/cds/event/css-seminar-september-2-oldham/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMIN
AR

\n

Friday\, September 2
\, 3:00-4:30 p.m.\nCenter for Social Complexity Suite\n3rd Flo
or Research Hall\, Fairfax Campus

\n

Matth
ew Oldham\, CSS PhD student\nGeorge Mason University

\n

To big wing\, or not to big wing\, now an answ
er

\n

The Churchillian quote “Never
\, in the field of human conflict\, was so much owed by so many to so few”
\, encapsulates perfectly the heroics of Royal Air Force (RAF) Fighter Com
mand (FC) during the Battle of Britain. Despite the undoubted heroics\, qu
estions remain about how FC employed the ‘so few’. In particular\, the que
stion as to whether FC should have employed the ‘Big Wing’ tactics\, as pe
r 12 Group\, or implement the smaller wings as per 11 Group\, remains a so
urce of much debate. In this paper\, I create an agent based model (ABM) s
imulation of the Battle of Britain\, which provides valuable in- sight int
o the key components that influenced the loss rates of both sides. It prov
ides mixed support for the tactics employed by 11 Group\, as the model ide
ntified numerous variables that impacted the success or otherwise of the B
ritish.

\n

Light refreshments will be served
.

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2448@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND\nT
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)\nA Retr
ospective. 45 Years of Computational Atomic and Molecular Physics: What
Have We Learned\nBarry Schneider\nApplied and Computational Mathematics Di
vision\nInformation Technology Laboratory\nNational Institute of Standards
and Technology\nGaithersburg\, MD\nAtomic and molecular physics was an ea
rly beneficiary of the development of electronic computation. Luckily\, mo
st of the interactions between electrons and nuclei are well understood an
d it appears that all that remains is to “turn the crank”. In retrospect\,
this was not that simple. These are complex many-body systems and in orde
r to overcome the exponential scaling of these computations with the numbe
r of particles\, clever algorithms and efficient codes needed to be develo
ped. In this talk I will describe a number of the important developments t
hat have taken place over the past four decades and how they have impacted
our qualitative and quantitative understanding of scattering processes an
d the interaction of radiation with matter.\nSeptember 12\, 2016\, 4:30 pm
\nExploratory Hall\, Room 3301\nFairfax Campus\nRefreshments will be serve
d at 4:15 PM.\n———————————————————————-\nFind the schedule at http://www.c
masc.gmu.edu/seminars.htm
DTSTART;TZID=America/New_York:20160912T163000
DTEND;TZID=America/New_York:20160912T174500
LOCATION:Fairfax Campus @ Exploratory Hall\, Room 3301
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – A Retrospective. 45 Years of
Computational Atomic and Molecular Physics: What Have We Learned – Barr
y Schneider
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-a-ret
rospective-45-years-of-computational-atomic-and-molecular-physics-what-hav
e-we/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Atomic and molecular physics was an early beneficiary of the d
evelopment of electronic computation. Luckily\, most of the interactions b
etween electrons and nuclei are well understood and it appears that all th
at remains is to “turn the crank”. In retrospect\, this was not that simpl
e. These are complex many-body systems and in order to overcome the expone
ntial scaling of these computations with the number of particles\, clever
algorithms and efficient codes needed to be developed. In this talk I will
describe a number of the important developments that have taken place ove
r the past four decades and how they have impacted our qualitative and qua
ntitative understanding of scattering processes and the interaction of rad
iation with matter.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1679@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 703-993-9298\; kunderwo@gmu.edu\; https://cos.gmu
.edu/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nRobert Axtell\, Co
mputational and Social Science Professor\nDepartment of Computational and
Data Sciences\nGeorge Mason University\nFriday\, September 16\, 3:00 p.m.
\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall\nBeyond Gi
brat’s Law: On the Growth of Firms One Worker at a Time\nThe stochastic th
eory of firm growth\, originating with Gibrat\, is not a theory of firm in
teractions but rather a model of an individual firm subject to exogenous s
hocks. Mathematically\, it is well-known that Gibrat’s formulation of firm
growth is a kind of random walk of log(firm size) and produces lognormal
distributions of sizes that are not stationary. Thus the theory has to be
supplemented with additional features\, as was done by Simon and others\,
in order to yield empirically-observed firm sizes\, i.e.\, a Pareto distri
bution with exponent near unity\, the so-called Zipf distribution. Here we
show that there are multiple problems associated with these variations on
Gibrat’s law\, having to do with fluctuations in firm sizes being too vol
atile to be meaningful economically\, with the Pareto exponents produced n
ot agreeing with empirical values generally\, and the inability to aggrega
te from a single firm to a population of firms without further specificati
ons that are unrealistic. We propose a different kind of stochastic growth
specification that couples the firms in an economy through labor flows an
d leads naturally to the Zipf distribution. We will argue that it is a kin
d of anti-Gibrat process insofar as it negatively couples firms—each time
one grows\, another shrinks. This alternative theory has a natural interpr
etation in terms of labor economics as job-to-job flows\, which are a larg
e and persistent feature of modern economies. We conclude by applying thes
e arguments to other social phenomena that have been modeled with Gibrat’s
law\, including membership in voluntary organizations\, sizes of cities\,
and the connectivity of networks.\nLight refreshments will be served.
DTSTART;TZID=America/New_York:20160916T150000
DTEND;TZID=America/New_York:20160916T163000
LOCATION:Center for Social Complexity Suite @ 3rd Floor Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Beyond Gibrat’s Law: On the
Growth of Firms One Worker at a Time – Robert Axtell
URL:http://cos.gmu.edu/cds/event/css-seminar-september-16-robert-axtell/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

COMPUTATIONAL SOCIAL SCIENCE FRI
DAY SEMINAR

\n

Robert Axtell
\, Computational and Social Science Professor\nDepartment of Computa
tional and Data Sciences\nGeorge Mason University

The stochasti
c theory of firm growth\, originating with Gibrat\, is not a theory of fir
m interactions but rather a model of an individual firm subject to exogeno
us shocks. Mathematically\, it is well-known that Gibrat’s formulation of
firm growth is a kind of random walk of log(firm size) and produces lognor
mal distributions of sizes that are not stationary. Thus the theory has to
be supplemented with additional features\, as was done by Simon and other
s\, in order to yield empirically-observed firm sizes\, i.e.\, a Pareto di
stribution with exponent near unity\, the so-called Zipf distribution. Her
e we show that there are multiple problems associated with these variation
s on Gibrat’s law\, having to do with fluctuations in firm sizes being too
volatile to be meaningful economically\, with the Pareto exponents produc
ed not agreeing with empirical values generally\, and the inability to agg
regate from a single firm to a population of firms without further specifi
cations that are unrealistic. We propose a different kind of stochastic gr
owth specification that couples the firms in an economy through labor flow
s and leads naturally to the Zipf distribution. We will argue that it is a
kind of anti-Gibrat process insofar as it negatively couples firms—each t
ime one grows\, another shrinks. This alternative theory has a natural int
erpretation in terms of labor economics as job-to-job flows\, which are a
large and persistent feature of modern economies. We conclude by applying
these arguments to other social phenomena that have been modeled with Gibr
at’s law\, including membership in voluntary organizations\, sizes of citi
es\, and the connectivity of networks.

\n

Light refreshments will be
served.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2467@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND TH
E DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)\nUnderst
anding and controlling the magnetic exchange of novel materials\nJames Gla
sbrenner\nDepartment of Computational and Data Sciences\, and\nComputation
al Materials Science Center\nGeorge Mason University\nFairfax\, VA\nSeptem
ber 19\, 2016\, 4:30 pm\nExploratory Hall\, Room 3301\nFairfax Campus\nThe
behavior of electrons in materials underpins key materials properties\, w
hich means that computing and understanding the electronic structure of va
rious materials systems is of vital importance. These computations are\, n
owadays\, often performed using density functional theory (DFT)\, a first-
principles methodology that is an important tool in the computational mate
rials scientist’s toolbox. One of the materials properties that is accessi
ble via DFT is magnetism\, and DFT can be used to study the magnetic inter
action between electrons (called the exchange interaction) in a material.
This involves constructing models such as the Heisenberg model and mapping
DFT calculations onto it\, which allows one to understand how tuning diff
erent features impacts important parameters such as the critical temperatu
re and the stability of magnetic ground state. This approach to studying m
agnetic materials is of particular appeal in the spin electronics field\,
where the encoding and processing information using the magnetic states of
electrons is of central importance. In this talk I will: 1) introduce the
basic concepts of computational materials science using DFT in an accessi
ble manner\, and 2) present calculations on two different materials where
I used DFT in conjunction with modeling to analyze the magnetic interactio
ns. The first presented material will be the dilute magnetic semiconductor
(Ba\, K)(Zn\, Mn)2As2\, which exhibits ferromagnetism when a small amount
of manganese and potassium are substituted into the material\, and where
changing the relative quantity of potassium influences the strength of the
magnetic interactions. The second presented material is MnAu2\, a magneti
c metal that has a cork-screw noncollinear magnetic ground state which can
be tuned in intriguing ways using pressure and chemical substitution. Usi
ng modeling in combination with DFT\, I will show how we are able to under
stand the nature of the microscopic magnetic interactions in each material
and that the microscopic mechanisms driving the the magnetic interactions
in both compounds is the same. These results can then be used to resolve
several experimental questions\, one of which had gone unaddressed for sev
eral decades.\nRefreshments will be served at 4:15 PM.\n——————————————————
—————-\nFind the schedule at http://www.cmasc.gmu.edu/seminars.htm
DTSTART;TZID=America/New_York:20160919T163000
DTEND;TZID=America/New_York:20160919T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Understanding and controllin
g the magnetic exchange of novel materials – James Glasbrenner
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-under
standing-and-controlling-the-magnetic-exchange-of-novel-materials-james-gl
asbren/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SC
IENCE CENTER AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 89
8-Sec 001)

\n

Understanding and co
ntrolling the magnetic exchange of novel materials

The behavior of electrons in materials under
pins key materials properties\, which means that computing and understandi
ng the electronic structure of various materials systems is of vital impor
tance. These computations are\, nowadays\, often performed using density f
unctional theory (DFT)\, a first-principles methodology that is an importa
nt tool in the computational materials scientist’s toolbox. One of the mat
erials properties that is accessible via DFT is magnetism\, and DFT can be
used to study the magnetic interaction between electrons (called the exch
ange interaction) in a material. This involves constructing models such as
the Heisenberg model and mapping DFT calculations onto it\, which allows
one to understand how tuning different features impacts important paramete
rs such as the critical temperature and the stability of magnetic ground s
tate. This approach to studying magnetic materials is of particular appeal
in the spin electronics field\, where the encoding and processing informa
tion using the magnetic states of electrons is of central importance. In t
his talk I will: 1) introduce the basic concepts of computational material
s science using DFT in an accessible manner\, and 2) present calculations
on two different materials where I used DFT in conjunction with modeling t
o analyze the magnetic interactions. The first presented material will be
the dilute magnetic semiconductor (Ba\, K)(Zn\, Mn)2As2\, which exhibits f
erromagnetism when a small amount of manganese and potassium are substitut
ed into the material\, and where changing the relative quantity of potassi
um influences the strength of the magnetic interactions. The second presen
ted material is MnAu2\, a magnetic metal that has a cork-screw noncollinea
r magnetic ground state which can be tuned in intriguing ways using pressu
re and chemical substitution. Using modeling in combination with DFT\, I w
ill show how we are able to understand the nature of the microscopic magne
tic interactions in each material and that the microscopic mechanisms driv
ing the the magnetic interactions in both compounds is the same. These res
ults can then be used to resolve several experimental questions\, one of w
hich had gone unaddressed for several decades.

\n

Refreshments will b
e served at 4:15 PM.

\n

———————————————————————-

\n

Find the sch
edule at http://www.cmasc.gmu.edu/seminars.htm

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2502@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/calendar/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nAustin Gerig\, Fin
ancial Economist\nOffice of Markets\nU.S. Securities and Exchange Commissi
on\nFriday\, September 23\, 2016\, 3:00 p.m.\nCenter for Social Complexit
y Suite\n3rd Floor\, Research Hall\nHigh-Frequency Trading Synchronizes Pr
ices in Financial Markets\nHigh-speed computerized trading\, often called
“high-frequency trading” (HFT)\, has increased dramatically in financial m
arkets over the last decade. In the US and Europe\, it now accounts for ne
arly one-half of all trades. Although evidence suggests that HFT increase
s market efficiency\, there are concerns it also adds to market instabilit
y\, especially during times of stress. Here\, I show that HFT synchronize
s prices in financial markets\, making the prices of related securities ch
ange contemporaneously. With a model\, I demonstrate how price synchroniz
ation leads to efficiency gains: prices are more accurate and transaction
costs are reduced. During times of stress\, however\, localized errors qu
ickly propagate through the financial system if safeguards are not in plac
e. This research highlights an important role that HFT plays in markets a
nd helps answer several puzzling questions that previously seemed difficul
t to explain: why HFT is so prevalent\, why HFT concentrates in certain se
curities and largely ignores others\, and finally\, how HFT can lower tran
saction costs yet still make profits.
DTSTART;TZID=America/New_York:20160923T150000
DTEND;TZID=America/New_York:20160923T163000
LOCATION:Center for Social Complexity Suite @ 3rd Floor\, Research Hall\, F
airfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – High-Frequency Trading Synch
ronizes Prices in Financial Markets- Austin Gerig
URL:http://cos.gmu.edu/cds/event/computational-social-science-friday-semina
r-high-frequency-trading-synchronizes-prices-in-financial-markets-austin-g
erig/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

High-speed com
puterized trading\, often called “high-frequency trading” (HFT)\, has incr
eased dramatically in financial markets over the last decade. In the US an
d Europe\, it now accounts for nearly one-half of all trades. Although ev
idence suggests that HFT increases market efficiency\, there are concerns
it also adds to market instability\, especially during times of stress. H
ere\, I show that HFT synchronizes prices in financial markets\, making th
e prices of related securities change contemporaneously. With a model\, I
demonstrate how price synchronization leads to efficiency gains: prices a
re more accurate and transaction costs are reduced. During times of stres
s\, however\, localized errors quickly propagate through the financial sys
tem if safeguards are not in place. This research highlights an important
role that HFT plays in markets and helps answer several puzzling question
s that previously seemed difficult to explain: why HFT is so prevalent\, w
hy HFT concentrates in certain securities and largely ignores others\, and
finally\, how HFT can lower transaction costs yet still make profits.

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2447@cos.gmu.edu/cds
DTSTAMP:20171214T023243Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)\nLocal
Structure Analysis in Simulated Materials via Voronoi Topology\nEmanuel A.
Lazar\nLaboratory for Research on the Structure of Matter\nSchool of Engi
neering and Applied Science\nUniversity of Pennsylvania\nPhiladelphia\, PA
\nDescribing how atoms are arranged in real and simulated materials is a v
ery natural problem that arises in numerous computational materials scienc
e applications. However\, aside from perfect crystals\, insightful yet tra
ctable descriptions of local arrangements of atoms can be tricky to develo
p. We consider several conventional order-parameter methods for describi
ng local structure and highlight their theoretical and practical limitatio
ns. We then introduce a topological approach more naturally suited for s
tructure analysis and highlight its versatility and robustness. In particu
lar\, the Voronoi tessellation method can aid in the study of materials at
high temperatures\, close to melting\, without uncontrolled modification
of raw data. Applications to the study of grain boundary evolution and mel
ting will be briefly presented.\nSeptember 26\, 2016\, 4:30 pm\nExplorator
y Hall\, Room 3301\nFairfax Campus\nRefreshments will be served at 4:15 PM
.\n———————————————————————-\nFind the schedule at http://www.cmasc.gmu.edu
/seminars.htm
DTSTART;TZID=America/New_York:20160926T163000
DTEND;TZID=America/New_York:20160926T174500
LOCATION:Fairfax Campus @ Exploratory Hall\, Room 3301
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Local Structure Analysis in
Simulated Materials via Voronoi Topology – Emanuel A. Lazar
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-local
-structure-analysis-in-simulated-materials-via-voronoi-topology-emanuel-a-
lazar/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Emanuel A. Lazar\nLaboratory for Research on the Structure
of Matter\nSchool of Engineering and Applied Science\nUniversi
ty of Pennsylvania\nPhiladelphia\, PA

\n

Describing how atoms a
re arranged in real and simulated materials is a very natural problem that
arises in numerous computational materials science applications. However\
, aside from perfect crystals\, insightful yet tractable descriptions of l
ocal arrangements of atoms can be tricky to develop. We consider several
conventional order-parameter methods for describing local structure and h
ighlight their theoretical and practical limitations. We then introduce
a topological approach more naturally suited for structure analysis and hi
ghlight its versatility and robustness. In particular\, the Voronoi tessel
lation method can aid in the study of materials at high temperatures\, clo
se to melting\, without uncontrolled modification of raw data. Application
s to the study of grain boundary evolution and melting will be briefly pre
sented.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1688@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 703-993-9298\; kunderwo@gmu.edu\; https://cos.gmu
.edu/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nWilliam G. Kennedy
\, Research Assistant Professor\nCenter for Social Complexity\nKrasnow Ins
titute for Advanced Study\nGeorge Mason University\nCSS Applied to a Mega-
Disaster and to Autonomous Behavior\nFriday\, September 30\, 3:00 p.m.\nCe
nter for Social Complexity Suite\n3rd Floor\, Research Hall\nABSTRACT: Th
is talk will focus on two recently funded projects. The first is to answer
how people would react to at nuclear WMD (weapon of mass destruction) eve
nt in a mega-city and a regional area. The task is to characterize the pop
ulation’s behavior over the hours\, days\, and few weeks following the eve
nt. This basic research project began during the summer and will involve t
wo faculty and two Graduate Research Assistants (GRAs) for 3 years. The pr
oject is being supported by the Defense Threat Reduction Agency (DTRA). Th
e second project is an effort to develop advanced engineering language\, s
ymbols\, and visualization to improve the systems engineering of complex a
nd increasingly autonomous systems. This project involves incorporating co
ncepts including beliefs and intents as part of the specifications and dev
elopment process for systems that are becoming more complex and autonomous
\, such as drones operating in the national air space. The project involve
s one faculty member and one GRA for one year and is sponsored by the Nati
onal Aeronautic and Space Administration (NASA) grant to LMI (Logistics Ma
nagement Inc.). These two project are part of the Center for Social Comple
xity within the Krasnow Institute for Advanced Study.
DTSTART;TZID=America/New_York:20160930T150000
DTEND;TZID=America/New_York:20160930T163000
LOCATION:Center for Social Complexity Suite @ 3rd Floor\, Research Hall\, F
airfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – CSS Applied to a Mega-Disast
er and to Autonomous Behavior – William Kennedy
URL:http://cos.gmu.edu/cds/event/css-seminar-september-30-william-kennedy/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Fri
day\, September 30\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall

\n

ABSTRA
CT: This talk will focus on
two recently funded projects. The first is to answer how people would reac
t to at nuclear WMD (weapon of mass destruction) event in a mega-city and
a regional area. The task is to characterize the population’s behavior ove
r the hours\, days\, and few weeks following the event. This basic researc
h project began during the summer and will involve two faculty and two Gra
duate Research Assistants (GRAs) for 3 years. The project is being support
ed by the Defense Threat Reduction Agency (DTRA). The second project is an
effort to develop advanced engineering language\, symbols\, and visualiza
tion to improve the systems engineering of complex and increasingly autono
mous systems. This project involves incorporating concepts including belie
fs and intents as part of the specifications and development process for s
ystems that are becoming more complex and autonomous\, such as drones oper
ating in the national air space. The project involves one faculty member a
nd one GRA for one year and is sponsored by the National Aeronautic and Sp
ace Administration (NASA) grant to LMI (Logistics Management Inc.). These
two project are part of the Center for Social Complexity within the Krasno
w Institute for Advanced Study.

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2473@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND\nT
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)\nEmbedd
ing Context in Intelligent User Interfaces Using Data Analytics\nFeras A.
Batarseh\nDepartment of Geography and Geoinformatics\nGeorge Mason Univers
ity\nFairfax\, VA\n \nOctober 3\, 2016\, 4:30 pm\nExploratory Hall\, Room
3301\nFairfax Campus\n \nFor Artificial Intelligence (AI) to reach its int
ended goals\, it must mitigate fears of singularity and illustrate its ben
efits to the human race. Many researchers in this field believe that conte
xt is a gateway to many of these positive aspects of intelligence. The imp
ortance of data analytics and context in many recent software research stu
dies have been rising significantly. Injecting context awareness into data
models has facilitated multiple facets of modeling\, increasing user sati
sfaction\, and increased the level of intelligence in software. Context ha
s been a major part of traditional AI\, lately however\, it is being deplo
yed to a wider spectrum of research topics through analytics such as human
cognition\, user interfaces design and development\, the internet of thin
gs\, the cloud\, software usability and many others. In this talk\, a ne
w context-aware method for emergency applications is introduced. An emerge
ncy is a situation that poses an immediate risk to life\, health\, propert
y or environment. There is an obvious gap in applying context to such crit
ical cases\; this talk will show that context awareness and intelligent us
er interfaces can play an important role with notifications and improve hu
man reactions during such unfortunate and unforeseen incidents.\nRefreshme
nts will be served at 4:15 PM.\n———————————————————————-\nFind the schedul
e at http://www.cmasc.gmu.edu/seminars.htm\n
DTSTART;TZID=America/New_York:20161003T163000
DTEND;TZID=America/New_York:20161003T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Embedding Context in Intelli
gent User Interfaces Using Data Analytics – Feras A. Batarseh
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-embed
ding-context-in-intelligent-user-interfaces-using-data-analytics-feras-a-b
atars/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Fo
r Artificial Intelligence (AI) to reach its intended goals\, it must mitig
ate fears of singularity and illustrate its benefits to the human race. Ma
ny researchers in this field believe that context is a gateway to many of
these positive aspects of intelligence. The importance of data analytics a
nd context in many recent software research studies have been rising signi
ficantly. Injecting context awareness into data models has facilitated mul
tiple facets of modeling\, increasing user satisfaction\, and increased th
e level of intelligence in software. Context has been a major part of trad
itional AI\, lately however\, it is being deployed to a wider spectrum of
research topics through analytics such as human cognition\, user interface
s design and development\, the internet of things\, the cloud\, software u
sability and many others. In this talk\, a new context-aware method for
emergency applications is introduced. An emergency is a situation that pos
es an immediate risk to life\, health\, property or environment. There is
an obvious gap in applying context to such critical cases\; this talk will
show that context awareness and intelligent user interfaces can play an i
mportant role with notifications and improve human reactions during such u
nfortunate and unforeseen incidents.

\n

Refreshments will be served a
t 4:15 PM.

\n

———————————————————————-

\n

Find the schedule at h
ttp://www.cmasc.gmu.edu/seminars.htm

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2565@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/calendar/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nAnnetta Burger\, C
SS PhD Student\nDepartment of Computational and Data Sciences\nGeorge Maso
n University\nFrom Networks to Recovery:\nEffects of social networks on co
mmunity resiliency\nin the face of flooding disasters\nFriday\, October 7\
, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall
\nABSTRACT: The value of social capital and social networks has increasing
ly been identified as a critical factor for adaptive capacity in disaster
recovery and community resilience. Inherent in an individual’s social ties
are available information\, resources and behaviors that can be accessed
in times of need. Simon Szreter and Michael Woolcock (2004) proposed socia
l capital framework within social networks that have been found to impact
disaster recovery differently. Bonding ties provide access to immediate re
lief and aid\, and bridging and bracing/linking ties provide pathways to l
onger-term survival resources such as recovery information and resource mo
bilization. An agent-based model of a community recovering from flood is p
roposed to test the utility of these different social network forms and co
mmunity groups in a period of disaster recovery.\nLight refreshments will
be served.\n
DTSTART;TZID=America/New_York:20161007T150000
DTEND;TZID=America/New_York:20161007T163000
LOCATION:Center for Social Complexity Suite @ 3rd Floor Research Hall\, Fa
irfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – From Networks to Recovery: E
ffects of social networks on community resiliency in the face of flooding
disasters – Annetta Burger
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-from-
networks-to-recovery-effects-of-social-networks-on-community-resiliency-in
-the-face-of-flooding-disasters-annetta-burger/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

ABSTRACT: The
value of social capital and social networks has increasingly been identif
ied as a critical factor for adaptive capacity in disaster recovery and co
mmunity resilience. Inherent in an individual’s social ties are available
information\, resources and behaviors that can be accessed in times of nee
d. Simon Szreter and Michael Woolcock (2004) proposed social capital frame
work within social networks that have been found to impact disaster recove
ry differently. Bonding ties provide access to immediate relief and aid\,
and bridging and bracing/linking ties provide pathways to longer-term surv
ival resources such as recovery information and resource mobilization. An
agent-based model of a community recovering from flood is proposed to test
the utility of these different social network forms and community groups
in a period of disaster recovery.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1690@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 703-993-9298\; kunderwo@gmu.edu\; https://cos.gmu
.edu/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nHemant Purohit\, A
ssistant Professor\nInformation Sciences and Technology\nGeorge Mason Univ
ersity\nMining Social Data for Public Behaviors to Improve Absorptive\nCap
acity of Organizations for Decision Support\nFriday\, October 14\, 3:00 p
.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall\nABSTRA
CT: Social Media or Web 2.0\, one source of Big Data\, has completely rev
olutionized information sourcing\, management\, and processing. The opport
unity to understand and exploit such unprecedented level of human interact
ion data can help public sector organizations improve their processes and
decision making\, such as for disaster management in building resilient sm
art cities. The data challenges of large scale volume of public data\, vel
ocity of content generation\, sparsity of data behaviors\, variety of lang
uage complexity and diversity of community demographics in this online int
eraction data present an exciting venue for improving science of public be
havior in mediated communication. For instance\, mining intentional behavi
ors of resource seeking on social media helps disaster response organizati
ons better coordinate resources\, given the mounting cost of response esti
mated to be 271 billion USD/yr by 2025.\nThis talk presents a Web informat
ion processing framework with examples to interpret\, manage\, and integra
te unstructured social media data generated by public (citizen sensors) in
to organizational workflows to improve their absorptive capacity of this u
nconventional data sources. Via use-cases of disaster response coordinatio
n\, and gender-based violence policy recommendation\, the talk will presen
t experiences and challenges in model public behaviors (e.g.\, intention\,
attitude) while tackling data challenges mentioned above. The talk will d
iscuss methods to perform such behavioral computing by fusing knowledge fr
om the Web resources (e.g.\, Wikipedia\, Linked Open Data) and socio-psych
ological theories of actionable behavior into statistical methods of text
mining\, and machine learning.\nHemant Purohit is an interdisciplinary\, c
omputational social science researcher and an assistant professor of Infor
mation Sciences and Technology at GMU. He was one of the ITU Young Innovat
or 2014 for UN’s ICT agency\, for winning a global challenge on Open Sourc
e Technologies for Disaster Management\, as well as one of the eight inter
national fellows of USAID\, Google and ICT4Peace foundation at a key human
itarian technology event of CrisisMappers\, ICCM-2013 at UN Nairobi. He ha
s presented several talks\, lectures\, and tutorials on social computing a
t prestigious venues including AAAI and SIAM conferences as well as publis
hed at and served as reviewer for peer-reviewed conferences and journals i
ncluding ICWSM\, WWW\, Journal of CSCW\, and ACM TIST. More about Hemant:
http://ist.gmu.edu/~hpurohit
DTSTART;TZID=America/New_York:20161014T150000
DTEND;TZID=America/New_York:20161014T163000
LOCATION:Center for Social Complexity Suite @ 3rd Floor\, Research Hall\, F
airfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Mining Social Data for Publi
c Behaviors to Improve Absorptive Capacity of Organizations for Decision S
upport – Hemant Purohit
URL:http://cos.gmu.edu/cds/event/css-seminar-october-14-hemant-purohit/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

ABSTRACT: Socia
l Media or Web 2.0\, one source of Big Data\, has completely revolutionize
d information sourcing\, management\, and processing. The opportunity to u
nderstand and exploit such unprecedented level of human interaction data c
an help public sector organizations improve their processes and decision m
aking\, such as for disaster management in building resilient smart cities
. The data challenges of large scale volume of public data\, velocity of c
ontent generation\, sparsity of data behaviors\, variety of language compl
exity and diversity of community demographics in this online interaction d
ata present an exciting venue for improving science of public behavior in
mediated communication. For instance\, mining intentional behaviors of res
ource seeking on social media helps disaster response organizations better
coordinate resources\, given the mounting cost of response estimated to b
e 271 billion USD/yr by 2025.

\n

This talk presents a We
b information processing framework with examples to interpret\, manage\, a
nd integrate unstructured social media data generated by public (citizen s
ensors) into organizational workflows to improve their absorptive capacity
of this unconventional data sources. Via use-cases of disaster response c
oordination\, and gender-based violence policy recommendation\, the talk will present expe
riences and challenges in model public behaviors (e.g.\, intention\, attit
ude) while tackling data challenges mentioned above. The talk will discuss
methods to perform such behavioral computing by fusing knowledge from the
Web resources (e.g.\, Wikipedia\, Linked Open Data) and socio-psychologic
al theories of actionable behavior into statistical methods of text mining
\, and machine learning.

\n

Hemant Purohit is an interdisciplinary\,
computational social science researcher and an assistant professor of Info
rmation Sciences and Technology at GMU. He was one of the ITU Young Innova
tor 2014 for UN’s ICT agency\, for winning a global challenge on Open Sour
ce Technologies for Disaster Management\, as well as one of the eight inte
rnational fellows of USAID\, Google and ICT4Peace foundation at a key huma
nitarian technology event of CrisisMappers\, ICCM-2013 at UN Nairobi. He h
as presented several
talks\, lectures\, and tutorials on social computing at prestigious
venues including AAAI and SIAM conferences as well as published at and se
rved as reviewer for peer-reviewed conferences and journals including ICWS
M\, WWW\, Journal of CSCW\, and ACM TIST. More about Hemant: http://ist.gmu.
edu/~hpurohit

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2563@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES:
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nLimit
ed Path Percolation in Complex Networks\nEduardo Lopez\, Asst. Professor\n
Department of Computational and Data Sciences\nGeorge Mason University\nFa
irfax\, VA\nOctober 17\, 2016\, 4:30 pm\nExploratory Hall\, Room 3301\nFai
rfax Campus\n\nWe propose a new percolation model in which reachability (a
generalization of connectivity) between any pair of nodes is defined on t
he basis of the relative increase in distance between nodes before and aft
er percolation removal or some other path lengthening process. Reachabilit
y is well justified for real-world networks where\, contrary to microscopi
c systems\, the ability to explore all possible paths of any length betwee
n nodes is not achievable. If path lenghtining exceeds an externally impos
ed fraction tau\, which reflects the specifics of the problem\, node pairs
are no longer reachable. This concept induces a new form of phase transit
ion\, which we call Limited Path Percolation (LPP). We find that LPP induc
es in many cases first-order phase transitions in contrast to percolation\
, which is second order. Also\, LPP predicts that networks are more fragil
e than what is suggested by conventional percolation models\, where effect
ively tau is considered infinite (any path length increase is acceptable).
In networks\, we find that Erdös-Rényi graphs and networks with steep p
ower-law distributions of connectivity exhibit a range of critically reach
able clusters between the new reachability transition and the conventional
percolation transition. Very heavy tailed power law distributions of conn
ectivity are generally robust to the introduction of the reachability conc
ept and behave as in conventional percolation. The models and results abov
e\, as well as some implications for areas such as epidemiology\, will be
discussed.\nRefreshments will be served at 4:15 PM.\n—————————————————————
——-\nFind the schedule at http://www.cmasc.gmu.edu/seminars.htm\n \n
DTSTART;TZID=America/New_York:20161017T163000
DTEND;TZID=America/New_York:20161017T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Limited Path Percolation in
Complex Networks – Eduardo Lopez
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-limit
ed-path-percolation-in-complex-networks-eduardo-lopez/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

We propose a new per
colation model in which reachability (a generalization of connectivity) be
tween any pair of nodes is defined on the basis of the relative increase i
n distance between nodes before and after percolation removal or some othe
r path lengthening process. Reachability is well justified for real-world
networks where\, contrary to microscopic systems\, the ability to explore
all possible paths of any length between nodes is not achievable. If path
lenghtining exceeds an externally imposed fraction tau\, which reflects th
e specifics of the problem\, node pairs are no longer reachable. This conc
ept induces a new form of phase transition\, which we call Limited Path Pe
rcolation (LPP). We find that LPP induces in many cases first-order phase
transitions in contrast to percolation\, which is second order. Also\, LPP
predicts that networks are more fragile than what is suggested by convent
ional percolation models\, where effectively tau is considered infinite (a
ny path length increase is acceptable). In networks\, we find that Erdös
-Rényi graphs and networks with steep power-law distributions of connectiv
ity exhibit a range of critically reachable clusters between the new reach
ability transition and the conventional percolation transition. Very heavy
tailed power law distributions of connectivity are generally robust to th
e introduction of the reachability concept and behave as in conventional p
ercolation. The models and results above\, as well as some implications fo
r areas such as epidemiology\, will be discussed.

\n

Refreshments wil
l be served at 4:15 PM.

\n

———————————————————————-

\n

Find the
schedule at http://www.cmasc.gmu.edu/seminars.htm

\n

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2624@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/calendar/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\n\nThe Non-randomne
ss of Random Dynamic Networks: A Baseline Modeling Approach\n \nJoseph Sha
heen\, CSS PhD Student\nGeorge Mason University\nFairfax\, VA\nOctober 21\
, 3:00-4:30 p.m.\nCenter for Social Complexity Suite\nResearch Hall\, 3rd
Floor\n \nIn an effort to model terrorist (ISIS/ISIL/DAESH) social media n
etworks\, a foundational model of dynamic social networks is necessary. Ov
er the last decade a concerted effort has been made from the various lines
of inquiry concerned with social networks to describe this model\, but ma
ny would argue that a comprehensive framework to describe changing\, fluct
uating social networks continues to elude us.\nIn this session\, I propose
the beginning and essential elements of a dynamic\, stochastic\, undirect
ed\, mathematically tractable model built through the use of an agent-base
d model utilizing simple rules. In other words\, I propose a generating fu
nction for dynamic social networks.\nThe model proposed preserves the fund
amental elements of random network generation and is in line with the earl
y network paradigm described by Erdos\, Renyi and Gilbert in the 1950s\, b
ut produces skewed and heavy tailed degree distributions in line with more
recent network models such as the Barabasi-Albert and Watts-Strogatz Mode
ls.\nI propose that the emergence of scale-free and small world properties
of a network can be produced using simple randomized rules. I also propos
e that partial conditional dependence\, or the relaxation of the dyadic in
dependence property championed by the statistical branch of network resear
ch is not entirely necessary for the generation of real world networks.\nF
inally\, I propose a new modeling element in the form of a coupled set of
time-based rules\, useful in the modeling and simulation of agent-based mo
dels that rely on social networks and furthermore argue its importance and
showcase its implications.\n
DTSTART;TZID=America/New_York:20161021T150000
DTEND;TZID=America/New_York:20161021T163000
LOCATION:Center for Social Complexity Suite\, 3rd Floor Research Hall
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – The Non-randomness of Random
Dynamic Networks: A Baseline Modeling Approach – Joseph Shaheen
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-the-n
on-randomness-of-random-dynamic-networks-a-baseline-modeling-approach-jose
ph-shaheen/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

COMPUTATIONAL SOCIAL SCIENCE FRI
DAY SEMINAR

\n

\n

The Non-randomness of Random Dynamic Networks:
A Baseline Modeling Approach

In an effort to model terrorist (ISIS/ISIL/DAES
H) social media networks\, a foundational model of dynamic social networks
is necessary. Over the last decade a concerted effort has been made from
the various lines of inquiry concerned with social networks to describe th
is model\, but many would argue that a comprehensive framework to describe
changing\, fluctuating social networks continues to elude us.

\n

In
this session\, I propose the beginning and essential elements of a dynamic
\, stochastic\, undirected\, mathematically tractable model built through
the use of an agent-based model utilizing simple rules. In other words\, I
propose a generating function for dynamic social networks.

\n

The mo
del proposed preserves the fundamental elements of random network generati
on and is in line with the early network paradigm described by Erdos\, Ren
yi and Gilbert in the 1950s\, but produces skewed and heavy tailed degree
distributions in line with more recent network models such as the Barabasi
-Albert and Watts-Strogatz Models.

\n

I propose that the emergence of
scale-free and small world properties of a network can be produced using
simple randomized rules. I also propose that partial conditional dependenc
e\, or the relaxation of the dyadic independence property championed by th
e statistical branch of network research is not entirely necessary for the
generation of real world networks.

\n

Finally\, I propose a new mode
ling element in the form of a coupled set of time-based rules\, useful in
the modeling and simulation of agent-based models that rely on social netw
orks and furthermore argue its importance and showcase its implications.
p>\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2587@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)\n \nEmb
edding Context in Intelligent User Interfaces Using Data Analytics\n \nDie
ter Pfoser\, Associate Professor\nDepartment of Geography and Geoinformati
cs\nGeorge Mason University\nFairfax\, VA\nOctober 24\, 2016\, 4:30 pm\nEx
ploratory Hall\, Room 3301\nFairfax Campus\nMaking sense of user-generated
Web content such as social media data\, blogs\, or even Wikipedia entries
poses interesting research challenge considering its lack of structure\,
amount\, and associated noise. This work introduces a range of online cont
ent summaries for such unstructured data. Besides the typical spatial\, te
mporal and thematic summaries\, we introduce two additional views. Geoeven
ts are emerging events that are limited in spatial scope hashtags and that
are detected automatically by comparing their spatial distribution to a g
lobal topic search.\nLinks-of-Interests (LOIs) are connection summaries be
tween geographic locations\, people and concepts. These content summaries
are available as part of the functionality of a Web-based tool that allows
for the interactive visualization\, querying and exploration of such unst
ructured data. This talk will discuss research results and demo the capabi
lities of the visualization prototype.\nRefreshments will be served at 4:1
5 PM.\n———————————————————————-\nFind the schedule at http://www.cmasc.gmu
.edu/seminars.htm\n
DTSTART;TZID=America/New_York:20161024T163000
DTEND;TZID=America/New_York:20161024T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Embedding Context in Intelli
gent User Interfaces Using Data Analytics – Dieter Pfoser
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-embed
ding-context-in-intelligent-user-interfaces-using-data-analytics-dieter-pf
oser/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Making sense of user-generated Web content such as social
media data\, blogs\, or even Wikipedia entries poses interesting research
challenge considering its lack of structure\, amount\, and associated noi
se. This work introduces a range of online content summaries for such unst
ructured data. Besides the typical spatial\, temporal and thematic summari
es\, we introduce two additional views. Geoevents are emerging events that
are limited in spatial scope hashtags and that are detected automatically
by comparing their spatial distribution to a global topic search.

\n

Links-of-Interests (LOIs) are connection summaries between geographic loc
ations\, people and concepts. These content summaries are available as par
t of the functionality of a Web-based tool that allows for the interactive
visualization\, querying and exploration of such unstructured data. This
talk will discuss research results and demo the capabilities of the visual
ization prototype.

\n

Refreshments will be served at 4:15 PM.

\n

———————————————————————-

\n

Find the schedule at http://www.cmasc.gm
u.edu/seminars.htm

\n

\n

div>
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2626@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 703-993-9298\; kunderwo@gmu.edu\; https://cos.gmu
.edu/cds/calendar/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR \nOn high performan
ce agent-based modeling\nEmmet Beeker\nPrincipal Simulation Modeling Engin
eer\nChristine Harvey\nSenior Simulation & Modeling Engineer\nMITRE Corpor
ation\nFriday\, October 28\, 3:00-4:30 p.m.\nCenter for Social Complexity
Suite\nResearch Hall\, 3rd Floor\n\n\n\nThis Friday seminar will deal with
a few of the major issues involved with creating and running high-perform
ance agent-based models. With modern computing hardware creating large sc
ale agent models is much easier. Furthermore\, running large scale design
s of experiments are now possible\, also. However\, what about very large
scale agent-based models\, ones where the number of agents reaches 100 mi
llion or more? And what of dealing with designs of experiments that have
10^50 points? Even modern hardware and software can be quickly overwhelme
d by the scale of an agent-based model or experiments using them. Under t
hese circumstances the science of high performance computing becomes espec
ially important. This talk will introduce a few of these important concep
ts from moving past time stepping to discrete event schedulers\, to the fi
ner points of threading a model on a shared memory machine or computing cl
uster. Many examples from practical\, real world\, problems will be used
to highlight the utility of these concepts.\n
DTSTART;TZID=America/New_York:20161028T150000
DTEND;TZID=America/New_York:20161028T163000
LOCATION:Center for Social Complexity Suite\, 3rd Floor Research Hall
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Emmet Beeker and Christine H
arvey
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-octob
er-28-emmet-beeker-christine-harvey/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

This Friday
seminar will deal with a few of the major issues involved with creating a
nd running high-performance agent-based models. With modern computing har
dware creating large scale agent models is much easier. Furthermore\, run
ning large scale designs of experiments are now possible\, also. However\
, what about very large scale agent-based models\, ones where the number o
f agents reaches 100 million or more? And what of dealing with designs of
experiments that have 10^50 points? Even modern hardware and software ca
n be quickly overwhelmed by the scale of an agent-based model or experimen
ts using them. Under these circumstances the science of high performance
computing becomes especially important. This talk will introduce a few of
these important concepts from moving past time stepping to discrete event
schedulers\, to the finer points of threading a model on a shared memory
machine or computing cluster. Many examples from practical\, real world\,
problems will be used to highlight the utility of these concepts.\n

\n

\n\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2590@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\n\nSci
entific Data Mining and Its Applications in Tropical Cyclone Research\n \n
Ruixin Yang\, Associate Professor\nDepartment of Geography and Geoinformat
ics\nGeorge Mason University\nFairfax\, VA\nOctober 31\, 4:30 pm\nExplorat
ory Hall\, Room 3301\nFairfax Campus\n\nIn this talk I will give an overvi
ew of of scientific data mining (SDM) followed by a few examples of SDM ap
plications to the analysis of tropical cyclones (TCs)\, focusing on their
intensity changes. Because rapidly intensifying (RI) tropical cyclones are
the major error sources in TC intensity forecasting\, association rules f
acilitate the RI process by mining for sets of conditions that have strong
interactions with rapidly intensifying TCs. The technique of association
rules explores associations among multiple conditions in a simple manner i
dentifying a predictor set with fewer factors but improved RI probabilitie
s. Furthermore\, in searching the “optimal” RI condition combinations\, a
peculiar condition combination was identified that gives a very high RI pr
obability. Such combination can be considered as a sufficient condition fo
r RI that almost guarantees that a RI will take place. Applications of cla
ssification techniques to the intensity forecasting will also be discussed
. Several drawbacks and future directions for SDM with the TC intensity ch
ange problem will be discussed at the end of the talk.\nRefreshments will
be served at 4:15 PM.\n———————————————————————-\nFind the schedule at http
://www.cmasc.gmu.edu/seminars.htm\n \n
DTSTART;TZID=America/New_York:20161031T163000
DTEND;TZID=America/New_York:20161031T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Scientific Data Mining and I
ts Applications in Tropical Cyclone Research – Ruixin Yang
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-scien
tific-data-mining-and-its-applications-in-tropical-cyclone-research-ruixin
-yang/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

In
this talk I will give an overview of of scientific data mining (SDM) foll
owed by a few examples of SDM applications to the analysis of tropical cyc
lones (TCs)\, focusing on their intensity changes. Because rapidly intensi
fying (RI) tropical cyclones are the major error sources in TC intensity f
orecasting\, association rules facilitate the RI process by mining for set
s of conditions that have strong interactions with rapidly intensifying TC
s. The technique of association rules explores associations among multiple
conditions in a simple manner identifying a predictor set with fewer fact
ors but improved RI probabilities. Furthermore\, in searching the “optimal
” RI condition combinations\, a peculiar condition combination was identif
ied that gives a very high RI probability. Such combination can be conside
red as a sufficient condition for RI that almost guarantees that a RI will
take place. Applications of classification techniques to the intensity fo
recasting will also be discussed. Several drawbacks and future directions
for SDM with the TC intensity change problem will be discussed at the end
of the talk.

\n

Refreshments will be served at 4:15 PM.

\n

—————
——————————————————-

\n

Find the schedule at http://www.cmasc.gmu.edu/
seminars.htm

\n

\n

\n

BODY>
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2905@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/calendar/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nIRS RESEARCH\, ANA
LYSIS AND STATISTICS\nFriday\, November 4\, 2016\, 3:00 p.m.\nCenter for
Social Complexity Suite\n3rd Floor\, Research Hall\nIntroduction\nJoseph S
haheen\, CSS PhD Student\nThe Network Picture of Labor Flow\nDr. Eduardo L
opez\, CSS Assistant Professor\nIn job markets\, some pairs of firms frequ
ently see workers changing jobs between them\, while at the same time othe
r firm pairs see little or no worker movement between them. This has led t
o the introduction of the so-called Labor-Flow Network\, a firm-to-firm ne
twork that acts as the scaffolding for job mobility. In this talk\, I pres
ent the first model for the dynamics of the workforce in the labor flow ne
twork with the help from data for two countries (Finland and Mexico) where
large scale microdata is available for individuals and their employers. T
he model correctly provides a new way to predict firm-size distributions b
ased on microscopic mechanisms\, and may in the future allow for the track
ing of employment and even tax evasion in some circumstances.\nAbility and
Ambition as Components of Inequality: A Simulation \nMs. Abigail Devereau
x\, Economic PhD Student\nA lot of attention has been paid to wealth inequ
ality since 2008-9. Many theories contend that regulations favorable to th
e wealthy and pernicious market effects might be to blame. However\, we ar
e interested to what degree natural characteristics of agents\, like their
preferences for career or money\, their ability to observe opportunities\
, and their level of ambition in seeking out new opportunities\, are compo
nents of inequality. We create a simple agent-based model to investigate t
he potential effects of natural characteristics on wealth inequality. We f
ind that these kinds of natural characteristics are likely components of w
ealth inequality\, though their effects are relatively small\, where most
of the inequality we see is due to a natural growth process.\nThe Impact o
f Investor Networks\n Matthew Oldham\, CSS PhD Student\nIn this se
ssion\, I will be demonstrating how investor networks and the dividends fr
om firms impact the behavior of financial markets. The model I will demons
trate will highlight the special properties of a scale-free network and ho
w management can inflate their stock price.\nGenerating Narrative from Ind
ividual-Level Data\nBrent Auble\, CSS PhD Student\nAgent-based models (ABM
s) and “big data” sets both contain / generate large volumes of disparate
information about individuals. In the case of ABMs\, the models generate
all information for all agents about what an agent did\, where it went\, w
hich other agents it interacted with\, and its reasons for choosing each a
ction. In the case of “big data”\, there can be large disparate data sets
in individuals\, sometimes containing too much information and sometimes
containing sparse or missing information\, but still allowing inferences o
n what an individual did over time. Typical approaches of selecting a few
numeric variables and averaging them and/or tracking their results over t
ime can lose significant details on the individual. This talk will discus
s approaches for taking the raw data from ABMs or other data sources and g
enerating narrative-form “life histories” of individuals as a method for m
aking sense of individual behavior. Also discussed will be methods for id
entifying which individuals are “interesting” — worth the time of a resear
cher to read through the agent’s history and or dig deeper into data.\nIRS
Presentation\n Mr. Michael Dunn and Colleagues\nCurrent resear
ch questions and challenges faced by IRS-RAS\nClosing Remarks\nRobert Axte
ll\, CSS Professor\n
DTSTART;TZID=America/New_York:20161104T150000
DTEND;TZID=America/New_York:20161104T163000
LOCATION:Center for Social Complexity Suite\, 3rd Floor Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – IRS Research\, Analysis and
Statistics
URL:http://cos.gmu.edu/cds/event/css-seminar-november-4-2016-irs-research-a
nalysis-and-statistics/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

In job markets\, some pairs of firms frequently se
e workers changing jobs between them\, while at the same time other firm p
airs see little or no worker movement between them. This has led to the in
troduction of the so-called Labor-Flow Network\, a firm-to-firm network th
at acts as the scaffolding for job mobility. In this talk\, I present the
first model for the dynamics of the workforce in the labor flow network wi
th the help from data for two countries (Finland and Mexico) where large s
cale microdata is available for individuals and their employers. The model
correctly provides a new way to predict firm-size distributions based on
microscopic mechanisms\, and may in the future allow for the tracking of e
mployment and even tax evasion in some circumstances.

A lot of attention
has been paid to wealth inequality since 2008-9. Many theories contend tha
t regulations favorable to the wealthy and pernicious market effects might
be to blame. However\, we are interested to what degree natural character
istics of agents\, like their preferences for career or money\, their abil
ity to observe opportunities\, and their level of ambition in seeking out
new opportunities\, are components of inequality. We create a simple agent
-based model to investigate the potential effects of natural characteristi
cs on wealth inequality. We find that these kinds of natural characteristi
cs are likely components of wealth inequality\, though their effects are r
elatively small\, where most of the inequality we see is due to a natural
growth process.

In this session\, I will be demonstrating how investor networks an
d the dividends from firms impact the behavior of financial markets. The m
odel I will demonstrate will highlight the special properties of a scale-f
ree network and how management can inflate their stock price.

Agent-based mode
ls (ABMs) and “big data” sets both contain / generate large volumes of dis
parate information about individuals. In the case of ABMs\, the models ge
nerate all information for all agents about what an agent did\, where it w
ent\, which other agents it interacted with\, and its reasons for choosing
each action. In the case of “big data”\, there can be large disparate da
ta sets in individuals\, sometimes containing too much information and som
etimes containing sparse or missing information\, but still allowing infer
ences on what an individual did over time. Typical approaches of selectin
g a few numeric variables and averaging them and/or tracking their results
over time can lose significant details on the individual. This talk will
discuss approaches for taking the raw data from ABMs or other data source
s and generating narrative-form “life histories” of individuals as a metho
d for making sense of individual behavior. Also discussed will be methods
for identifying which individuals are “interesting” — worth the time of a
researcher to read through the agent’s history and or dig deeper into dat
a.

\n

IRS Presentation\n M
r. Michael Dunn and Colleagues

\n

Current research questions and chal
lenges faced by IRS-RAS

\n

Closing Remarks\n
strong>Robert Axtell\, CSS Professor

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2874@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nRobus
t Estimation of Value-at-Risk for Quantitative Risk Management: Applicatio
ns to Climatology\, Insurance\, Accidents and Financial Analysis\nSabyasac
hi Guharay\nU.S. Department of the Treasury\nWashington\, D.C.\nNovember 7
\, 4:30 pm\nExploratory Hall\, Room 3301\nFairfax Campus\n\nEstablishing r
obust quantitative metrics which allow decision makers to determine the am
ount of risk in a system with extreme loss events is a problem of interest
in many scientific fields. One of the fundamental metrics which is univer
sally accepted in all fields of risk management is the quantity known as V
alue-at-Risk (VaR). A subfield of risk management\, modern Operational Ris
k Management (ORM)\, closely investigates methodologies on robustly estima
ting VaR\, “Robust Estimation of VaR.” Currently\, academic researchers an
d industry practitioners are actively looking at ways to make this estimat
e more statistically robust and accurate with minimal assumption requireme
nts.\nIn this talk I will present two new quantitative approaches for esti
mating VaR that are agnostic regarding the relationship between frequency
and severity: (1) Data Partition of Frequency and Severity (DPFS) using K-
means to estimate VaR\; (2) Distribution based partitioning (DBP) of frequ
ency and severity using copulas. Verification is conducted on five simulat
ed scenario datasets while validation is conducted on five publicly availa
ble datasets from four different domains: –US Financial Indices data of
Standard & Poor’s 500 and Dow Jones Industrial Average\; –Chemical Loss
spills as tracked by the US National Coast Guard\; –Australian automobil
e accidents\; –US hurricane data. It is observed that previous VaR calcu
lations inaccurately estimate the VaR for 80% of the cases in simulated da
ta and 60% of the cases in real-world data studies while new methodologies
attains accurate VaR estimates which are within the 95% confidence interv
al bounds for both simulated and real-world data.\nRefreshments will be se
rved at 4:15 PM.\n———————————————————————-\nFind the schedule at http://ww
w.cmasc.gmu.edu/seminars.htm\n \n
DTSTART;TZID=America/New_York:20161107T163000
DTEND;TZID=America/New_York:20161107T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Robust Estimation of Value-a
t-Risk for Quantitative Risk Management: Applications to Climatology\, In
surance\, Accidents and Financial Analysis – Sabyasachi Guharay
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-robus
t-estimation-of-value-at-risk-for-quantitative-risk-management-application
s-to-c/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Establishing robust quantitative metrics which allow de
cision makers to determine the amount of risk in a system with extreme los
s events is a problem of interest in many scientific fields. One of the fu
ndamental metrics which is universally accepted in all fields of risk mana
gement is the quantity known as Value-at-Risk (VaR). A subfield of risk ma
nagement\, modern Operational Risk Management (ORM)\, closely investigates
methodologies on robustly estimating VaR\, “Robust Estimation of VaR.” Cu
rrently\, academic researchers and industry practitioners are actively loo
king at ways to make this estimate more statistically robust and accurate
with minimal assumption requirements.

\n

In this talk I will present
two new quantitative approaches for estimating VaR that are agnostic regar
ding the relationship between frequency and severity: (1) Data Partition o
f Frequency and Severity (DPFS) using K-means to estimate VaR\; (2) Distri
bution based partitioning (DBP) of frequency and severity using copulas. V
erification is conducted on five simulated scenario datasets while validat
ion is conducted on five publicly available datasets from four different d
omains: –US Financial Indices data of Standard & Poor’s 500 and Dow Jone
s Industrial Average\; –Chemical Loss spills as tracked by the US Nation
al Coast Guard\; –Australian automobile accidents\; –US hurricane data
. It is observed that previous VaR calculations inaccurately estimate the
VaR for 80% of the cases in simulated data and 60% of the cases in real-wo
rld data studies while new methodologies attains accurate VaR estimates wh
ich are within the 95% confidence interval bounds for both simulated and r
eal-world data.

Mass
shootings unfold quickly and are rarely foreseen by victims. Increasingly\
, training is provided to increase chances of surviving active shooter sce
narios\, usually emphasizing\, “Run\, Hide\, Fight.” Evidence from prior m
ass shootings suggests that casualties may be limited should the shooter e
ncounter unarmed resistance prior to the arrival of law enforcement office
rs (LEOs). An agent-based model (ABM) explored the potential for limiting
casualties should a small proportion of potential victims swarm a gunman\,
as occurred on a train from Amsterdam to Paris in 2015. Results suggest t
hat even with a miniscule probability of overcoming a shooter\, fighters m
ay save lives but put themselves at increased risk. While not intended to
prescribe a course of action\, the model suggests the potential for a redu
ction in casualties in active shooter scenarios.

\n

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2591@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n(CSI 898-Sec 001)\nUsing
Coarse-Grained Models to Access Expanded Length and Time Scales for Nanos
cience Applications\nK. Michael Salerno\, Jr.\nCenter for Computational Ma
terials Science\nNaval Research Laboratory\nWashington D.C.\nNovember 14\,
4:30 pm\nExploratory Hall\, Room 3301\nFairfax Campus\nPolymers and other
soft materials have an important role as a coating for nanoscale building
blocks like metallic nanoparticles (NPs) and nanorods. This coating media
tes interactions between these building blocks and their environment. Atom
istic molecular dynamics (MD) simulations are ideal for examining the role
of chemistry and atomic interactions at the sub-nanometer scale\, for exa
mple in the interactions between a NP and a solvent\, or between pairs of
NPs. Unfortunately\, atomistic MD simulations are limited to lengths of or
der 50 nm and times of order 50 ns. The time scale limitation precludes mo
deling nanoscale self-assembly\, and limits dynamic simulations to extreme
ly high rates of deformation or thermalforcing. These simulations are also
limited to sizes that represent a small number of NPs\, making it impossi
ble to model large assembled structures.\nFaced with these limitations\, w
e have developed coarse-grained (CG) models of polyethylene\, a simple pol
ymer used to coat NPs and nanorods. These models have enabled simulations
of bulk polymer melts that overcome the limits of atomistic MD by providin
g a computational speedup of greater than 104 while retaining fundamental
details at the sub-nanometer scale. These details produce the viscoelastic
properties and semi-crystalline behavior that are intrinsic to polyethyle
ne and that are missed by generic CG models. When applied to a NP coating
the CG models capture the coating morphology\, indicating the value of usi
ng these CG models in nanoscale applications.\nRefreshments will be served
at 4:15 PM.\n———————————————————————-\nFind the schedule at http://www.cm
asc.gmu.edu/seminars.htm
DTSTART;TZID=America/New_York:20161114T163000
DTEND;TZID=America/New_York:20161114T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Using Coarse-Grained Models
to Access Expanded Length and Time Scales for Nanoscience Applications – K
. Michael Salerno\, Jr.
URL:http://cos.gmu.edu/cds/event/descriptive-title-colloquium-of-the-comput
ational-materials-science-center-and-the-department-of-computational-and-d
ata-sciences-using-coarse-grained-models-to-access-expanded-length-and-tim
e-scal/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Polymers and other soft materials have an important r
ole as a coating for nanoscale building blocks like metallic nanoparticles
(NPs) and nanorods. This coating mediates interactions between these buil
ding blocks and their environment. Atomistic molecular dynamics (MD) simul
ations are ideal for examining the role of chemistry and atomic interactio
ns at the sub-nanometer scale\, for example in the interactions between a
NP and a solvent\, or between pairs of NPs. Unfortunately\, atomistic MD s
imulations are limited to lengths of order 50 nm and times of order 50 ns.
The time scale limitation precludes modeling nanoscale self-assembly\, an
d limits dynamic simulations to extremely high rates of deformation or the
rmalforcing. These simulations are also limited to sizes that represent a
small number of NPs\, making it impossible to model large assembled struct
ures.

\n

Faced with these limitations\, we have developed coarse-grai
ned (CG) models of polyethylene\, a simple polymer used to coat NPs and na
norods. These models have enabled simulations of bulk polymer melts that o
vercome the limits of atomistic MD by providing a computational speedup of
greater than 104 while retaining fundamental details at the sub-nanometer
scale. These details produce the viscoelastic properties and semi-crystal
line behavior that are intrinsic to polyethylene and that are missed by ge
neric CG models. When applied to a NP coating the CG models capture the co
ating morphology\, indicating the value of using these CG models in nanosc
ale applications.

\n

Refreshments will be served at 4:15 PM.

\n

———————————————————————-

\n

Find the schedule at http://www.cmasc.gmu
.edu/seminars.htm

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3167@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/calendar/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nHarold Walbert\, C
SS PhD Student\nDepartment of Computational and Data Sceinces\nGeorge Maso
n University\nConnecting R and NetLogo\n\nFriday\, November 18\, 20016\, 3
:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor Research Hall\nThi
s talk will focus on the specifics of how you connect R to NetLogo. The op
en source agent based modeling platform NetLogo is used to create many age
nt based models. R is a powerful open source programming language that off
ers a wide array of tools for cleaning\, analyzing\, modeling and visualiz
ing data. The first example will focus on how to get started using the wel
l known Schelling segregation model from the models library in NetLogo. Th
e talk will progress with examples of how I have used these tools to analy
ze and understand my own agent based models. Topics covered will include d
ata import (agents\, networks)\, data cleaning\, running parameter sweeps\
, repeatability and replication\, as well as ways to integrate code with d
ata to help with repeatability and replication.\nThe talk will be followed
by a Q&A session along with light refreshments.
DTSTART;TZID=America/New_York:20161118T150000
DTEND;TZID=America/New_York:20161118T163000
LOCATION:Center for Social Complexity Suite @ 3rd Floor\, Research Hall\,
Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Connecting R and NetLogo – H
arold Walbert
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-conne
cting-r-and-netlogo-harold-walbert/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

This talk will focus on the specifics of
how you connect R to NetLogo. The open source agent based modeling platfo
rm NetLogo is used to create many agent based models. R is a powerful open
source programming language that offers a wide array of tools for cleanin
g\, analyzing\, modeling and visualizing data. The first example will focu
s on how to get started using the well known Schelling segregation model f
rom the models library in NetLogo. The talk will progress with examples of
how I have used these tools to analyze and understand my own agent based
models. Topics covered will include data import (agents\, networks)\, data
cleaning\, running parameter sweeps\, repeatability and replication\, as
well as ways to integrate code with data to help with repeatability and re
plication.

\n

The talk will be followed by a Q&A session along with light refreshmen
ts.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3165@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CMASC Colloquia
CONTACT:Estela Blaisten\; 703-993-1988\; blaisten@gmu.edu\; http://www.cmas
c.gmu.edu/seminars.htm
DESCRIPTION:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER\nAND T
HE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (\nCSI 898-Sec 001)\nTopo
logical Quantification of Microstructures\nTom Wanner\nDepartment of Mathe
matical Sciences\nGeorge Mason University\nFairfax\, VA\nNovember 21\, 201
6\, 4:30 p.m.\nExploratory Hall\, Room 3301\nFairfax Campus\n \nMany appli
ed processes generate complex microstructures or patterns which are hard t
o quantify due to the lack of any underlying regular structure. These patt
erns may evolve with time or include some element of stochasticity. The re
sulting variations in the detail structure frequently force one to concent
rate on rougher geometric features. From a mathematical point of view\, se
veral notions from algebraic topology suggest themselves as natural quanti
fication tools in such a setting. In this talk I will describe some of t
hese tools\, in particular homology and persistent homology\, and how they
can be efficiently computed using open source software. I will also prese
nt some applications motivated by materials science problems.\nRefreshment
s will be served at 4:15 PM.\n———————————————————————-\nFind the schedule
at http://www.cmasc.gmu.edu/seminars.htm\n
DTSTART;TZID=America/New_York:20161121T163000
DTEND;TZID=America/New_York:20161121T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DE
PARTMENT OF COMPUTATIONAL AND DATA SCIENCES – Topological Quantification o
f Microstructures – Tom Wanner
URL:http://cos.gmu.edu/cds/event/colloquium-of-the-computational-materials-
science-center-and-the-department-of-computational-and-data-sciences-topol
ogical-quantification-of-microstructures-tom-wanner/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Many applied processes generate comple
x microstructures or patterns which are hard to quantify due to the lack o
f any underlying regular structure. These patterns may evolve with time or
include some element of stochasticity. The resulting variations in the de
tail structure frequently force one to concentrate on rougher geometric fe
atures. From a mathematical point of view\, several notions from algebraic
topology suggest themselves as natural quantification tools in such a set
ting. In this talk I will describe some of these tools\, in particular h
omology and persistent homology\, and how they can be efficiently computed
using open source software. I will also present some applications motivat
ed by materials science problems.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3250@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\n \nFriday\, Decemb
er 2\, 3:00-4:30 p.m.\nCenter for Social Complexity Suite\nResearch Hall\,
3rd Floor\n\nTalha Oz\, CSS PhD Student\nDepartment of Computational and
Data Sciences\nGeorge Mason University\nAttribution of Responsibility and
Blame Regarding a Man-made Disaster: #FlintWaterCrisis\nAttribution of re
sponsibility and blame are important topics in political science especiall
y as individuals tend to think of political issues in terms of questions o
f responsibility\, and as blame carries far more weight in voting behavior
than that of credit. However\, surprisingly\, there is a paucity of studi
es on the attribution of responsibility and blame in the field of disaster
research.\nThe Flint water crisis is a story of government failure at all
levels. By studying microblog posts about it\, we understand how citizens
assign responsibility and blame regarding such a man-made disaster online
. We form hypotheses based on social scientific theories in disaster resea
rch and then operationalize them on unobtrusive\, observational social med
ia data. In particular\, we investigate the following phenomena: the sourc
e for blame\; the partisan predisposition\; the concerned geographies\; an
d the contagion of complaining.\nThis paper adds to the sociology of disas
ters research by exploiting a new\, rarely used data source (the social we
b)\, and by employing new computational methods (such as sentiment analysi
s and retrospective cohort study design) on this new form of data. In this
regard\, this work should be seen as the first step toward drawing more c
hallenging inferences on the sociology of disasters from “big social data”
.\nYang Zhou\, CSS PhD Student\nDepartment of Computational and Data Scien
ces\nGeorge Mason University\nThe Origin of Agriculture in the Peiligang C
ulture: An Agent-based Modeling Approach\nThe emergence of agriculture pla
yed an important role in human history as it allowed people to move from a
nomadic to a sedentary life. This not only provided abundant food\, but a
lso sufficient numbers of non-cultivating specialists\, which are necessar
y conditions for the rise of civilization. However\, questions about how a
nd why agriculture originated have remained controversial. This paper expl
ores the origin hypotheses of agriculture\, using the canonical theory of
social complexity as a framework to the transition from hunter-gatherer to
agricultural societies in the region of the Peiligang culture in China ba
sed on existing literature\, and develops an agent-based model to simulate
the transition process. The model assumes that the combination of populat
ion growth and gaining knowledge on plants drove the transition to agricul
ture. Results show that based on the basic hypotheses and assumptions\, th
e model is able to generate the key phases that are identical with the exi
sting literatures.
DTSTART;TZID=America/New_York:20161202T150000
DTEND;TZID=America/New_York:20161202T163000
LOCATION:Center for Social Complexity Suite @ 3rd Floor\, Research Hall\, F
airfax\, Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Attribution of Responsibilit
y and Blame Regarding a Man-made Disaster: #FlintWaterCrisis – Talha Oz
and The Origin of Agriculture in the Peiligang Culture: An Agent-based M
odeling Approach – Yang Zhou
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-attri
bution-of-responsibility-and-blame-regarding-a-man-made-disaster-flintwate
rcrisis-talha-oz-and-the-origin-of-agriculture-in-the-peiligang-culture-a/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Attribution of Responsibility and Blame
Regarding a Man-made Disaster: #FlintWaterCrisis

\n

Attribu
tion of responsibility and blame are important topics in political science
especially as individuals tend to think of political issues in terms of q
uestions of responsibility\, and as blame carries far more weight in votin
g behavior than that of credit. However\, surprisingly\, there is a paucit
y of studies on the attribution of responsibility and blame in the field o
f disaster research.

\n

The Flint water crisis is a story of governme
nt failure at all levels. By studying microblog posts about it\, we unders
tand how citizens assign responsibility and blame regarding such a man-mad
e disaster online. We form hypotheses based on social scientific theories
in disaster research and then operationalize them on unobtrusive\, observa
tional social media data. In particular\, we investigate the following phe
nomena: the source for blame\; the partisan predisposition\; the concerned
geographies\; and the contagion of complaining.

\n

This paper adds t
o the sociology of disasters research by exploiting a new\, rarely used da
ta source (the social web)\, and by employing new computational methods (s
uch as sentiment analysis and retrospective cohort study design) on this n
ew form of data. In this regard\, this work should be seen as the first st
ep toward drawing more challenging inferences on the sociology of disaster
s from “big social data”.

T
he Origin of Agriculture in the Peiligang Culture: An Agent-based Modeling
Approach

\n

The emergence of agriculture played an imp
ortant role in human history as it allowed people to move from a nomadic t
o a sedentary life. This not only provided abundant food\, but also suffic
ient numbers of non-cultivating specialists\, which are necessary conditio
ns for the rise of civilization. However\, questions about how and why agr
iculture originated have remained controversial. This paper explores the o
rigin hypotheses of agriculture\, using the canonical theory of social com
plexity as a framework to the transition from hunter-gatherer to agricultu
ral societies in the region of the Peiligang culture in China based on exi
sting literature\, and develops an agent-based model to simulate the trans
ition process. The model assumes that the combination of population growth
and gaining knowledge on plants drove the transition to agriculture. Resu
lts show that based on the basic hypotheses and assumptions\, the model is
able to generate the key phases that are identical with the existing lite
ratures.

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3345@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nRobert Axtell\, Pr
ofessor\nComputational Social Science\nDepartment of Computational and Dat
a Sciences\nFoundations of Agent Computing for Economics and Finance\nFrid
ay\, February 3\, 2017\nCenter for Social Complexity Suite\n3rd Floor\, Re
search Hall\n\nIn many ways economics and finance are fields for which the
strengths of agent computing should lead researchers to become ‘early ado
pters\,’ at least in comparison to less quantitative social sciences. Whil
e this has happened to some extent\, it is also true that methodological n
orms are strong in economics and finance\, meaning that innovations will a
lways face resistance. In this talk I will preview the ideas from a paper\
, co-authored with J. Doyne Farmer of the University of Oxford’s Complexit
y Economics Programme\, geared toward enticing economists toward agent-bas
ed modeling. I will contrast the process-based\, procedural explanations o
f social phenomena that result from agent computing with the substantive b
ut often static explanations that are more conventional in economic theory
. The role of agents who learn will be highlighted. Next\, the ability of
agent computing to use all available hardware resources—the ‘small compile
time\, large run time’ character of ABMs–will be surveyed and compared wi
th standard numerical economics. Then we ask whether having better\, more
user friendly software could substantially increase the rate of adoption o
f agents among economists. Finally\, we suggest that technologies for syst
ematic parallelization of agent models might go far as a ‘killer app’ inso
far as it would permit the creation of large-scale models applicable to a
wide variety of policy problems. For each of these arguments an illustrati
ve example will be provided. I will conclude with a brief discussion of bo
ttlenecks that appear to be limiting the progress of agent computing in ec
onomics and finance at the present time.
DTSTART;TZID=America/New_York:20170203T150000
DTEND;TZID=America/New_York:20170203T163000
LOCATION:Center for Social Complexity Suite\, 3rd Floor\, Research Hall\, F
airfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Foundations of Agent Computing for E
conomics and Finance – Robert Axtell
URL:http://cos.gmu.edu/cds/event/computational-social-science-foundations-o
f-agent-computing-for-economics-and-finance-robert-axtell/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

In many ways economics and finance are fields for w
hich the strengths of agent computing should lead researchers to become ‘e
arly adopters\,’ at least in comparison to less quantitative social scienc
es. While this has happened to some extent\, it is also true that methodol
ogical norms are strong in economics and finance\, meaning that innovation
s will always face resistance. In this talk I will preview the ideas from
a paper\, co-authored with J. Doyne Farmer of the University of Oxford’s C
omplexity Economics Programme\, geared toward enticing economists toward a
gent-based modeling. I will contrast the process-based\, procedural explan
ations of social phenomena that result from agent computing with the subst
antive but often static explanations that are more conventional in economi
c theory. The role of agents who learn will be highlighted. Next\, the abi
lity of agent computing to use all available hardware resources—the ‘small
compile time\, large run time’ character of ABMs–will be surveyed and com
pared with standard numerical economics. Then we ask whether having better
\, more user friendly software could substantially increase the rate of ad
option of agents among economists. Finally\, we suggest that technologies
for systematic parallelization of agent models might go far as a ‘killer a
pp’ insofar as it would permit the creation of large-scale models applicab
le to a wide variety of policy problems. For each of these arguments an il
lustrative example will be provided. I will conclude with a brief discussi
on of bottlenecks that appear to be limiting the progress of agent computi
ng in economics and finance at the present time.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3376@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\n\nHowie Huang\, As
sociate Professor\nDepartment of Electrical and Computer Engineering\nGeor
ge Washington University\n\nHigh-Performance Computing Systems for Big Gra
ph Analytics\n\nFriday\, February 10\, 3:00-4:30 p.m.\nCenter for Social C
omplexity Suite\nResearch Hall\, 3rd Floor\nBig data are ubiquitous and gr
aph-based datasets are especially interesting with broader impacts in soci
al networks\, biological networks\, and cybersecurity. In this talk\, I wi
ll discuss a number of our recent projects (Enterprise SC’15\, iBFS SIGMOD
’16\, G-Store SC’16\, and Graphene FAST’17) and present our efforts on des
igning and developing high-performance graph algorithms and systems. In pa
rticular\, I will discuss several novel techniques on addressing the compu
tational and I/O challenges in graph computing. Furthermore\, I will prese
nt our ongoing work on utilizing these graph systems for understanding and
analyzing complex network data.\nDr. Howie Huang is an Associate Professo
r in Department of Electrical and Computer Engineering at the George Washi
ngton University. His research interests are in the areas of computer syst
ems and architecture\, including cloud computing\, big data\, high-perform
ance computing and storage systems. His work on big graph analytics has ra
nked highly on both the Graph500 and Green Graph500 benchmarks. Dr. Huang
is a recipient of the NSF CAREER Award\, Comcast Technology Research and D
evelopment Fund\, NVIDIA Academic Partnership Award\, IBM Real Time Innova
tion Faculty Award\, and GWU School of Engineering and Applied Science Out
standing Young Researcher Award. His projects won the Best Poster Award at
PACT’11\, ACM Undergraduate Student Research Competition at SC’12\, and a
finalist for the Best Student Paper Award at SC’11. He completed his Ph.D
. in the Department of Computer Science at the University of Virginia.\nht
tp://www.seas.gwu.edu/~howie/\n
DTSTART;TZID=America/New_York:20170210T150000
DTEND;TZID=America/New_York:20170210T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – High-Performance Computing Systems f
or Big Graph Analytics – Howie Huang
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-high-
performance-computing-systems-for-big-graph-analytics-howie-huang/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Big data are ubiquitous and
graph-based datasets are especially interesting with broader impacts in so
cial networks\, biological networks\, and cybersecurity. In this talk\, I
will discuss a number of our recent projects (Enterprise SC’15\, iBFS SIGM
OD’16\, G-Store SC’16\, and Graphene FAST’17) and present our efforts on d
esigning and developing high-performance graph algorithms and systems. In
particular\, I will discuss several novel techniques on addressing the com
putational and I/O challenges in graph computing. Furthermore\, I will pre
sent our ongoing work on utilizing these graph systems for understanding a
nd analyzing complex network data.

\n

Dr. Howie Huang is an Associate
Professor in Department of Electrical and Computer Engineering at the Geo
rge Washington University. His research interests are in the areas of comp
uter systems and architecture\, including cloud computing\, big data\, hig
h-performance computing and storage systems. His work on big graph analyti
cs has ranked highly on both the Graph500 and Green Graph500 benchmarks. D
r. Huang is a recipient of the NSF CAREER Award\, Comcast Technology Resea
rch and Development Fund\, NVIDIA Academic Partnership Award\, IBM Real Ti
me Innovation Faculty Award\, and GWU School of Engineering and Applied Sc
ience Outstanding Young Researcher Award. His projects won the Best Poster
Award at PACT’11\, ACM Undergraduate Student Research Competition at SC’1
2\, and a finalist for the Best Student Paper Award at SC’11. He completed
his Ph.D. in the Department of Computer Science at the University of Virg
inia.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3352@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nKevin Comer\, PhD
Candidate\nComputational Social Science\nDepartment of Computational and D
ata Sciences\nAssessing Adverse Selection in the Individual Health Insuran
ce Market using Agent-Based Modeling and Simulation\nFriday\, February 17\
, 2017 = 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Resear
ch Hall\n\nAdverse selection is the phenomenon of “rigged trades” created
by asymmetric information between buyers and sellers. This becomes signifi
cant in the case of health insurance\, where a buyer may know more about h
is or her health than the insurer. The concern is that widespread adverse
selection may lead to a “death spiral”\, where premiums become too costly
for healthy people to afford\, and the only subscribers left are unhealthy
people requiring costly health care. Both before and after the passing of
the Patient Protection and Affordable Care Act (PPACA\, also known as “Ob
amacare”) in 2013\, the concern for adverse selection has been assessed by
a number of different methods of economic modeling\, most notably game th
eory models\, econometrics\, and microsimulation. Kevin Comer utilizes age
nt-based modeling to assess the emergence and effects of adverse selection
on a simulated individual health insurance market\, and to test the param
eters of already existing policy (coverage and individual mandates\, risk
corridors\, medical loss ratios\, and health insurance exchanges) to asses
s their long-term feasibility.\nAbout the presenter: Kevin Comer is a Sen
ior Simulation Modeling Engineer at MITRE Corporation. He received his B.S
in Systems Engineering and Economics from the University of Virginia\, an
d his M.S. in Operations Research from George Mason University. He is curr
ently a Computational and Social Science Ph.D. Candidate in the Department
of Computational and Data Sciences at George Mason University. Kevin is s
cheduled to defend his dissertation in Spring 2017.
DTSTART;TZID=America/New_York:20170217T150000
DTEND;TZID=America/New_York:20170217T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Assessing Adverse Selection in the I
ndividual Health Insurance Market using Agent-Based Modeling and Simulatio
n – Kevin Comer
URL:http://cos.gmu.edu/cds/event/computational-social-science-assessing-adv
erse-selection-in-the-individual-health-insurance-market-using-agent-based
-modeling-and-simulation-kevin-comer/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Adverse s
election is the phenomenon of “rigged trades” created by asymmetric inform
ation between buyers and sellers. This becomes significant in the case of
health insurance\, where a buyer may know more about his or her health tha
n the insurer. The concern is that widespread adverse selection may lead t
o a “death spiral”\, where premiums become too costly for healthy people t
o afford\, and the only subscribers left are unhealthy people requiring co
stly health care. Both before and after the passing of the Patient Protect
ion and Affordable Care Act (PPACA\, also known as “Obamacare”) in 2013\,
the concern for adverse selection has been assessed by a number of differe
nt methods of economic modeling\, most notably game theory models\, econom
etrics\, and microsimulation. Kevin Comer utilizes agent-based modeling to
assess the emergence and effects of adverse selection on a simulated indi
vidual health insurance market\, and to test the parameters of already exi
sting policy (coverage and individual mandates\, risk corridors\, medical
loss ratios\, and health insurance exchanges) to assess their long-term fe
asibility.

\n

About the presenter:
Kevin Comer is a Senior Simulation Modeling Engi
neer at MITRE Corporation. He received his B.S in Systems Engineering and
Economics from the University of Virginia\, and his M.S. in Operations Res
earch from George Mason University. He is currently a Computational and So
cial Science Ph.D. Candidate in the Department of Computational and Data S
ciences at George Mason University. Kevin is scheduled to defend his disse
rtation in Spring 2017.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3409@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nYang Zhou\, CSS Ph
D Student\nDepartment of Computational and Data Sciences\nGeorge Mason Uni
versity\nThe Origin of Agriculture in the Peiligang Culture: An Agent-base
d Modeling Approach\nFriday\, February 24\, 3:00 p.m.\nCenter for Social C
omplexity Suite\n3rd Floor\, Research Hall\nThe emergence of agriculture p
layed an important role in human history as it allowed people to move from
a nomadic (i.e. hunter-gather) to a sedentary (i.e. agricultural) lifesty
le. This shift in lifestyle not only provided abundant food\, but also suf
ficient numbers of non-cultivating specialists\, which are necessary condi
tions for the rise of a civilization. However\, questions about how and wh
y agriculture originated have remained controversial. This paper explores
the origin hypotheses of agriculture\, using the canonical theory of socia
l complexity as a framework to study the transition from hunter-gatherer t
o agricultural societies in the region of the Peiligang in China based on
existing literature\, and develops an agent-based model to simulate the tr
ansition process. The model assumes that a combination of population growt
h and gaining knowledge on plants drove the transition from hunter-gathere
r to agriculture. Results show that based on the basic hypotheses and assu
mptions\, the model is able to generate the key phases that are identical
with the existing literature on such a transaction.\nThis session will be
live-streamed on the newly created CSS program YouTube channel.
DTSTART;TZID=America/New_York:20170224T150000
DTEND;TZID=America/New_York:20170224T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – The Origin of Agriculture in the Pei
ligang Culture: An Agent-based Modeling Approach – Yang Zhou
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-the-o
rigin-of-agriculture-in-the-peiligang-culture-an-agent-based-modeling-appr
oach-yang-zhou/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

The Origin of A
griculture in the Peiligang Culture: An Agent-based Modeling Approach

\n

Fr
iday\, February 24\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall

\n

The emergence of agricultur
e played an important role in human history as it allowed people to move f
rom a nomadic (i.e. hunter-gather) to a sedentary (i.e. agricultural) life
style. This shift in lifestyle not only provided abundant food\, but also
sufficient numbers of non-cultivating specialists\, which are necessary co
nditions for the rise of a civilization. However\, questions about how and
why agriculture originated have remained controversial. This paper explor
es the origin hypotheses of agriculture\, using the canonical theory of so
cial complexity as a framework to study the transition from hunter-gathere
r to agricultural societies in the region of the Peiligang in China based
on existing literature\, and develops an agent-based model to simulate the
transition process. The model assumes that a combination of population gr
owth and gaining knowledge on plants drove the transition from hunter-gath
erer to agriculture. Results show that based on the basic hypotheses and a
ssumptions\, the model is able to generate the key phases that are identic
al with the existing literature on such a transaction.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3505@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 703-993-9298\; kunderwo@gmu.edu\; http://cos.gmu.
edu/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nSalwa Ismail\, PhD
Student\nComputational Social Science\nDepartment of Computational and Da
ta Sciences\nGeorge Mason University\n Towards an ABM for Civil Revolution
:\nModeling Emergence of Protesters\, Military Decisions\, and Resulting S
tate of the Institution\n\nFriday\, March 3\, 3:00-4:30 p.m.\nCenter for S
ocial Complexity Suite\nResearch Hall\, 3rd Floor\nThe recent string of ev
ents in the Middle East\, dubbed as Arab Spring transcended rapidly. There
was no mechanism to predict them or their outcome. While there are a few
models that forecast rebellion\, most of them do not take into account the
ability of different factors\, such as emotional threshold\, of both the
citizens and military and their the ability to be influenced by vision of
what is going around the agent geographically\, along with the influence o
f media/communication channels\, to form a realm of influence and affect t
he actions of the agents simultaneously. This paper explores an agent-base
d model whose agents react based on economic and emotional levels and a re
bellion ensues. Once the rebellion has begun\, there are several other fac
tors in this agent-based model that decide the outcome of the rebellion in
cluding agents being killed\, their geographic vision\, their inclement to
wards news/media\, being influenced by current events\, and also their per
sonality type of A or B\; all these factors combined together affect the d
ynamics of the unanticipated revolution. The results of the model are rend
ered in a short duration of time\, as one would expect of revolutions\, ex
cept for those that plunder into a civil war state. The model could be use
d as one of many components for forecasting future rebellions that have a
combination of factors present\, as those discussed this paper.\nThis sess
ion will be live-streamed on the newly created CSS program YouTube channel
.
DTSTART;TZID=America/New_York:20170303T150000
DTEND;TZID=America/New_York:20170303T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Towards an ABM for Civil Revolution:
Modeling Emergence of Protesters\, Military Decisions\, and Resulting St
ate of the Institution – Salwa Ismail
URL:http://cos.gmu.edu/cds/event/computational-social-science-towards-an-ab
m-for-civil-revolution-modeling-emergence-of-protesters-military-decisions
-and-resulting-state-of-the-institution-salwa-ismail/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

The recent string of event
s in the Middle East\, dubbed as Arab Spring transcended rapidly. There wa
s no mechanism to predict them or their outcome. While there are a few mod
els that forecast rebellion\, most of them do not take into account the ab
ility of different factors\, such as emotional threshold\, of both the cit
izens and military and their the ability to be influenced by vision of wha
t is going around the agent geographically\, along with the influence of m
edia/communication channels\, to form a realm of influence and affect the
actions of the agents simultaneously. This paper explores an agent-based m
odel whose agents react based on economic and emotional levels and a rebel
lion ensues. Once the rebellion has begun\, there are several other factor
s in this agent-based model that decide the outcome of the rebellion inclu
ding agents being killed\, their geographic vision\, their inclement towar
ds news/media\, being influenced by current events\, and also their person
ality type of A or B\; all these factors combined together affect the dyna
mics of the unanticipated revolution. The results of the model are rendere
d in a short duration of time\, as one would expect of revolutions\, excep
t for those that plunder into a civil war state. The model could be used a
s one of many components for forecasting future rebellions that have a com
bination of factors present\, as those discussed this paper.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3538@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nAntoine Mandel\, A
ssociate Professor\nCES-Centre d’Economie de la Sorbonne\nEndogenous Growt
h in Production Networks\nFriday\, March 24\,2017\, 3:00-4:30 p.m.\nCenter
for Social Complexity Suite\nResearch Hall\, 3rd Floor\nWe investigate th
e interplay between technological change and macroeconomic dynamics in an
agent-based model of the formation of production networks. On the one hand
\, production networks form the structure that determines economic dynamic
s in the short run. On the other hand\, their evolution reflects the long-
term impacts of competition and innovation on the economy. We account for
process innovation via increasing variety in the input mix and hence incre
asing connectivity in the net- work. In turn\, product innovation induces
a direct growth of the firm’s productivity and the potential destruction o
f links. The interplay between both processes generate complex technologic
al dynamics in which phases of process and product innovation successively
dominate. The model reproduces a wealth of stylized facts about industria
l dynamics and technological progress\, in particular the persistence of h
eterogeneity among firms and Wright’s law for the growth of productivity w
ithin a technological paradigm. We illustrate the potential of the model f
or the analysis of industrial policy via a preliminary set of policy exper
iments in which we investigate the impact on innovators’ success of feed-i
n tariffs and of priority market access.
DTSTART;TZID=America/New_York:20170324T150000
DTEND;TZID=America/New_York:20170324T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Endogenous Growth in Production Netw
orks – Antoine Mandel
URL:http://cos.gmu.edu/cds/event/computational-social-science-endogenous-gr
owth-in-production-networks-antoine-mandel/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

We investigate the interplay between technological change a
nd macroeconomic dynamics in an agent-based model of the formation of prod
uction networks. On the one hand\, production networks form the structure
that determines economic dynamics in the short run. On the other hand\, th
eir evolution reflects the long-term impacts of competition and innovation
on the economy. We account for process innovation via increasing variety
in the input mix and hence increasing connectivity in the net- work. In tu
rn\, product innovation induces a direct growth of the firm’s productivity
and the potential destruction of links. The interplay between both proces
ses generate complex technological dynamics in which phases of process and
product innovation successively dominate. The model reproduces a wealth o
f stylized facts about industrial dynamics and technological progress\, in
particular the persistence of heterogeneity among firms and Wright’s law
for the growth of productivity within a technological paradigm. We illustr
ate the potential of the model for the analysis of industrial policy via a
preliminary set of policy experiments in which we investigate the impact
on innovators’ success of feed-in tariffs and of priority market access.
p>\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3603@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nJacqueline Kazil\,
PhD Student\nComputational Social Science Program\nDepartment of Computat
ional and Data Sciences\nGeorge Mason University\nMesa: An ABM Modeling Fr
amework in Python 3\nFriday\, March 31\, 3:00-4:30 p.m.\nCenter for Social
Complexity Suite\nResearch Hall\, 3rd Floor\nAgent-based modeling was lac
king in the Python community until Mesa. Mesa is an open-source\, liberall
y licensed\, agent-based modeling framework built in Python 3. It allows u
sers to quickly build models by providing a series of reusable components
off of which to build. This includes things like agents\, space\, and time
. Mesa is also flexible and decoupled for greater efficiency. It has a bac
k-end that handles the model processing and a browser-based front-end that
handles the visualization. It also allows for custom visualizations to be
added\, as well. Lastly\, users can use the Python ecosystem for analyzin
g data with ease with tools like Jupyter notebooks and Pandas. The goal of
Mesa is to provide a Python alternative to other agent-based modeling fra
meworks such as Netlogo and Mason.
DTSTART;TZID=America/New_York:20170331T150000
DTEND;TZID=America/New_York:20170331T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Mesa: An ABM Modeling Framework in P
ython 3 – Jacqueline Kazil
URL:http://cos.gmu.edu/cds/event/computational-social-science-mesa-an-abm-m
odeling-framework-in-python-3-jacqueline-kazil/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Agent-based m
odeling was lacking in the Python community until Mesa. Mesa is an open-so
urce\, liberally licensed\, agent-based modeling framework built in Python
3. It allows users to quickly build models by providing a series of reusa
ble components off of which to build. This includes things like agents\, s
pace\, and time. Mesa is also flexible and decoupled for greater efficienc
y. It has a back-end that handles the model processing and a browser-based
front-end that handles the visualization. It also allows for custom visua
lizations to be added\, as well. Lastly\, users can use the Python ecosyst
em for analyzing data with ease with tools like Jupyter notebooks and Pand
as. The goal of Mesa is to provide a Python alternative to other agent-bas
ed modeling frameworks such as Netlogo and Mason.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3604@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATONAL SOCIAL SCIENCE SEMINAR\nQing Tian\, Assistant Prof
essor\nComputational Social Science Program\nDepartment of Computational a
nd Data Sciences\nGeorge Mason University\nSustainable rural development:
a complex systems approach to policy analysis\nFriday\, April 7\, 2017\,
3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall\n
\nThe livelihoods of Chinese rural households have been undergoing a trans
formation amid rapid urbanization. Though participation in the urban econo
my has led to improved rural living standards\, rural income has consisten
tly lagged behind urban income\, and a broader prosperity gap persists bet
ween urban and rural areas. Meanwhile\, increasing non farm income is asso
ciated with the decline of agriculture\, especially in those regions with
relatively high industrial development. In this seminar I present an agent
-based model developed to explore future rural development. The model repr
esents land-use and livelihood decisions of farmer households in the Poyan
g Lake area\, informed by household surveys and interviews. The model expe
riments aid our understanding about (1) the potential effects of three sub
sidy policies in villages with different farmland endowments and at differ
ent stages of urbanization\, and (2) the resilience of rural development u
nder plausible environmental and economic shocks. I discuss how policy may
need to differentiate across locations and adapt in the near future to ef
fectively promote rural development amid social and environmental changes.
DTSTART;TZID=America/New_York:20170407T150000
DTEND;TZID=America/New_York:20170407T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Sustainable rural development: a com
plex systems approach to policy analysis – Qing Tian
URL:http://cos.gmu.edu/cds/event/computational-social-science-sustainable-r
ural-development-a-complex-systems-approach-to-policy-analysis-qing-tian/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

The livelihoods of Chines
e rural households have been undergoing a transformation amid rapid urbani
zation. Though participation in the urban economy has led to improved rura
l living standards\, rural income has consistently lagged behind urban inc
ome\, and a broader prosperity gap persists between urban and rural areas.
Meanwhile\, increasing non farm income is associated with the decline of
agriculture\, especially in those regions with relatively high industrial
development. In this seminar I present an agent-based model developed to e
xplore future rural development. The model represents land-use and livelih
ood decisions of farmer households in the Poyang Lake area\, informed by h
ousehold surveys and interviews. The model experiments aid our understandi
ng about (1) the potential effects of three subsidy policies in villages w
ith different farmland endowments and at different stages of urbanization\
, and (2) the resilience of rural development under plausible environmenta
l and economic shocks. I discuss how policy may need to differentiate acro
ss locations and adapt in the near future to effectively promote rural dev
elopment amid social and environmental changes.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3528@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 703-993-9298\; kunderwo@gmu.edu\; http://cos.gmu.
edu/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nThomas Pike\, PhD
Student\nComputational Social Science\nDepartment of Computational and Dat
a Sciences\nGeorge Mason University\nComplex Intelligence Preparation of t
he Battlefield: \nAn Effort to Operationalize the Integration of Political
Theory to Improve Analysis Across the Intelligence Enterprise \nFriday\,
April 14\, 2017\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floo
r\, Research Hall\nFifteen years of conflict have shown severe limitations
in the United States’ ability to influence foreign populations in pursuit
of national objectives. Intertwined within these challenges is the diffic
ulty the U.S. Intelligence Enterprise has in integrating recent research t
o more effectively analyze foreign populations and support decision makers
. The introduction of a Complex Adaptive Systems based meta-framework for
intelligence analysts\, supported by an agent based model can reduce the c
ost of analysts learning and applying new research. As a first attempt we
adopt the Army’s current framework Intelligence Preparation of the Battlef
ield (IPB) and begin to formulate one possible meta-perspective Complex In
telligence Preparation of the Battlefield. Complex IPB has the potential t
o be a constantly improving model that integrates new and emerging theorie
s from economics\, communication\, politics\, demography\, game theory and
social network analysis to analyze the emergence and contagion of civil c
onflict in local populations. Complex IPB can assist in identifying region
s of potential instability before escalation. Finally\, a quasi-global sen
sitivity analysis identifies effective and efficient policy levers in the
face of limited resources.
DTSTART;TZID=America/New_York:20170414T150000
DTEND;TZID=America/New_York:20170414T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Complex Intelligence Preparation of
the Battlefield: An Effort to Operationalize the Integration of Political
Theory to Improve Analysis Across the Intelligence Enterprise – Thomas Pik
e
URL:http://cos.gmu.edu/cds/event/computational-social-science-complex-intel
ligence-preparation-of-the-battlefield-an-effort-to-operationalize-the-int
egration-of-political-theory-to-improve-analysis-across-the-intelligence-e
nterpri/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Complex Intelligenc
e Preparation of the Battlefield: \nAn Effort to Op
erationalize the Integration of Political Theory to Improve Analysis Acros
s the Intelligence Enterprise

\n

Friday\, April 14\, 2017\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall

\n

Fifteen years of conflict have shown severe limitations in th
e United States’ ability to influence foreign populations in pursuit of na
tional objectives. Intertwined within these challenges is the difficulty t
he U.S. Intelligence Enterprise has in integrating recent research to more
effectively analyze foreign populations and support decision makers. The
introduction of a Complex Adaptive Systems based meta-framework for intell
igence analysts\, supported by an agent based model can reduce the cost of
analysts learning and applying new research. As a first attempt we adopt
the Army’s current framework Intelligence Preparation of the Battlefield (
IPB) and begin to formulate one possible meta-perspective Complex Intellig
ence Preparation of the Battlefield. Complex IPB has the potential to be a
constantly improving model that integrates new and emerging theories from
economics\, communication\, politics\, demography\, game theory and socia
l network analysis to analyze the emergence and contagion of civil conflic
t in local populations. Complex IPB can assist in identifying regions of p
otential instability before escalation. Finally\, a quasi-global sensitivi
ty analysis identifies effective and efficient policy levers in the face o
f limited resources.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3632@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:Computational Social Science Seminar\nOPEN MIC\nFriday\, April
21\, 3:00-4:30 p.m.\nCenter for Social Complexity Suite\nResearch Hall\, 3
rd Floor\nThe CSS seminar format for Friday\, April 21 will be an “Open Mi
c” session for CSS PhD students to present their research ideas to their p
eers prior to starting their projects. Peer feedback in encouraged at this
event\nSome points for presenters to consider are:\n\nDo you have a paper
that is ‘stalled’ and in need of some help to push it to the finish line?
\nIs one of your models producing interesting results but also doing wacky
things?\nDo you have exciting results but can’t figure out how to visuali
ze/display them?\nDo you need advice on how to calibrate/estimate your the
oretical model with data?\n\nPlease visit www.cos.gmu.edu/cds/calendar/to
see list of upcoming seminar speakers.\nWe hope to see you on Friday\, Apr
il 21.
DTSTART;TZID=America/New_York:20170421T150000
DTEND;TZID=America/New_York:20170421T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Open Mic – CSS PhD Students
URL:http://cos.gmu.edu/cds/event/computational-social-science-open-mic-css-
phd-students/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

The CSS seminar format for Friday\, April 21
will be an “Open Mic” session for CSS PhD students to present their resea
rch ideas to their peers prior to starting their projects. Peer feedback i
n encouraged at this event

\n

Some points fo
r presenters to consider are:

\n

\n

Do you have a paper that is ‘
stalled’ and in need of some help to push it to the finish line?

\n

Is one of your models producing interesting results but also doing wacky
things?

\n

Do you have exciting results but can’t figure out how to
visualize/display them?

\n

Do you need advice on how to calibrate/
estimate your theoretical model with data?

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3614@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nStephen Davies\nHa
nnah Zontine\nDepartment of Computer Science\nUniversity of Mary Washingto
n\nThe ‘Hidden Trump Model’: Modeling social desirability bias through ABM
s\nSocial desirability bias is a tendency people have to lie about their
opinions if they perceive they will be judged or rejected. We present an O
pinion Dynamics model in which agents may not be truthful about their opin
ions when they interact with their social circle. We model two processes t
hrough which agents influence one another: an online anonymous process in
which agents can interact with anyone and do not fear social rejection\, a
nd a face-to-face process where they interact only with friends and may fe
el compelled to conform. In a political setting\, this would apply to a ra
ce in which one of the candidates bears a social stigma and therefore some
agents are reluctant to voice support for him or her. The results that th
ese nonlinear and asymmetrical processes will have on the overall electora
te are not obvious\, and are well-suited to an agent-based study.\nWe hypo
thesize that this model will produce a “poll bias” of the kind we saw in t
he 2016 Presidential election — i.e.\, a significant difference between th
e number of agents who say they will vote for a candidate and the number w
ho actually do so on election day. We present an analysis of this “Hidden
Trump model” and describe the way in which poll bias depends on the streng
th of the various interaction processes.
DTSTART;TZID=America/New_York:20170428T150000
DTEND;TZID=America/New_York:20170428T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE -The ‘Hidden Trump Model’: Modeling so
cial desirability bias through ABMs – Stephen Davis & Hannah Zontine
URL:http://cos.gmu.edu/cds/event/computational-social-science-the-hidden-tr
ump-model-modeling-social-desirability-bias-through-abms-stephen-davis-han
nah-zontine/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Social desirability bias is a tend
ency people have to lie about their opinions if they perceive they will be judged or rejected. We pr
esent an Opinion Dynamics model in which agents may not be truthful about their opinions when they interact with their social circle. We model two processes through which agents influence one another: an online anonymous pro
cess in which agents can interact with anyone and
do not fear social rejection\, and a face-to-face process where they interact only with friends and may feel compelled to conform. In a political setting\, this would apply t
o a race in which one of the candidates bears a so
cial stigma and therefore some agents are reluctan
t to voice support for him or her. The results tha
t these nonlinear and asymmetrical processes will
have on the overall electorate are not obvious\, and are well-suited to an
agent-based study.

\n

We hypothesize that this model will p
roduce a “poll bias” of the kind we saw in the 2016 Presidential election
— i.e.\, a significant dif
ference between the number of agents who say they will vote for a candidat
e and the number who actually do so on election day. We present an analysis of this “Hidden Trump mo
del” and describe the way in which poll bias depends on the strength of the various interaction proc
esses.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3699@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:Computational Social Science Friday Seminar\nPaul Albert\, Inde
pendent Scholar\nAnalyzing and Visualizing Data with Tableau\nFriday\, May
5\, 2017 – 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Re
search Hall\nAbstract: In January of 2017\, Forbes Magazine listed the
top technical job skills showing the highest growth in demand from 2011 to
2015. The number three position\, with 1\,581% growth\, was Tableau\, a s
oftware solution that helps people see and understand their data.\nTableau
offers free licenses for academic research.\nIn this session\, Paul Alber
t will:\n\nProvide a hands-on overview of Tableau to show how it can help
people do more with their data\nShow examples of Tableau data visualizatio
ns relevant to the CSS world\nDiscuss how Tableau might be able to alter p
aradigms for sharing academic findings\nDiscuss free resources available t
o learn more about Tableau\n\nPaul recently retired from Tableau and has s
tarted graduate studies with the GMU Art History program. His initial focu
s is on applying quantitative analysis and social theory to art markets. H
is secondary focus is on exploring how products like Tableau can be used t
o support new ways of presenting academic research and findings.\nWhile at
Tableau\, Paul coordinated and conducted over 60 training events for over
1\,100 participants. Paul was also one of four finalists\, out of a field
of over 200 contestants\, in the annual Tableau “Viz Wiz” data visualizat
ion contest for 2016.
DTSTART;TZID=America/New_York:20170505T150000
DTEND;TZID=America/New_York:20170505T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Analyzing and Visualizing Data with
Tableau – Paul Albert
URL:http://cos.gmu.edu/cds/event/computational-social-science-analyzing-and
-visualizing-data-with-tableau-paul-albert/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Abstract: In January of 2017\, Forbes Magazine listed the
top technical job skills showing the highest growth in demand from 2011 to
2015. The number three position\, with 1\,581% growth\, was Tableau\, a s
oftware solution that helps people see and understand their data.

\n

Tableau offers free licenses for academic research.

\n

In this sessio
n\, Paul Albert will:

\n

\n

Provide a hands-on overview of Tablea
u to show how it can help people do more with their data

\n

Show ex
amples of Tableau data visualizations relevant to the CSS world

\n

Discuss how Tableau might be able to alter paradigms for sharing academic
findings

\n

Discuss free resources available to learn more about Ta
bleau

\n

\n

Paul recently retired from Tableau and has started g
raduate studies with the GMU Art History program. His initial focus is on
applying quantitative analysis and social theory to art markets. His secon
dary focus is on exploring how products like Tableau can be used to suppor
t new ways of presenting academic research and findings.

\n

While at
Tableau\, Paul coordinated and conducted over 60 training events for over
1\,100 participants. Paul was also one of four finalists\, out of a field
of over 200 contestants\, in the annual Tableau “Viz Wiz” data visualizati
on contest for 2016.

The Mercatus Center at George Mason Un
iversity recently launched QuantGov\, an open-source policy analytics plat
form designed to facilitate policy-relevant research. QuantGov deploys t
ext analysis and machine-learning algorithms to identify the latent govern
ance indicators buried in policy documents\, such as legislation or admini
strative code. QuantGov grew out of the RegData project\, which was design
ed to capture novel metrics in regulations that would advance our understa
nding of the United States’ federal regulatory process in ways that were p
reviously infeasible. QuantGov is the next generation in that project’s e
volution.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3968@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 703-993-5017\; jmarr2@masonlive.gmu.edu\; https://cos
.gmu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR \nOscar Olmedo\
, PhD\nData Scientist\nCACI International\, Inc.\nCalibration for Probabil
istic Classification\nMonday\, August 28\, 4:30-5:45\nExploratory Hall\, R
oom 3301\nABSTRACT: This talk will be a review of calibration methods fo
r classifiers that make probabilistic predictions on a scale 0 to 1. It is
known that certain classification methods\, such as Naïve Bayes or Random
Forest make biased predictions that to not match the true posterior proba
bilities. By calibrating the predictions made by classifiers the true pro
bability of the predicted class can be determined. This type of calibratio
n can be crucial for real-world decision making problems in medicine\, bus
iness\, marketing\, and finance. In this talk I will focus on applications
in marketing.\n\nPart two: Marketing yourself for future careers outside
of academia\nIt is known that the number of jobs in academia is not rising
as fast as the number of PhD’s graduating. Currently a new career option
is available to these PhDs\, the Data Scientist. But how does one make the
transition out of academia to this hot new field? I will discuss strategi
es for marketing yourself as well as tools necessary to be successful in y
our transition.\n\nDr. Oscar Olmedo is an alumnus of George Mason Universi
ty who studied physics in undergrad (2004)\, Computational Sciences and In
formatics Masters (2007)\, and Computational Sciences and Informatics PhD
(2011) with a concentration in solar physics under Dr. Jie Zhang. After gr
aduating in 2011\, Dr. Olmedo went on to NRL as an NRC fellow for two year
s\, and briefly worked at NASA Goddard for a few months in 2013 before mov
ing to Syntasa\, a startup focusing on ecommerce/marketing analytics. In 2
015\, he moved to CACI to work on cyber security research as a DARPA contr
actor.\nA copy of Dr. Olmedo’s presentation is found here: OLMEDO_PRESENT
ATION_8.28.17
DTSTART;TZID=America/New_York:20170828T163000
DTEND;TZID=America/New_York:20170828T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Calibration for p
robabilistic classification – Oscar Olmedo
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-calibration-for-probabilistic-classification-olmedo/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
25/2017/08/me.jpg\;216\;216\,medium\;http://cos.gmu.edu/cds/wp-content/upl
oads/sites/25/2017/08/me.jpg\;216\;216\,large\;http://cos.gmu.edu/cds/wp-c
ontent/uploads/sites/25/2017/08/me.jpg\;216\;216\,full\;http://cos.gmu.edu
/cds/wp-content/uploads/sites/25/2017/08/me.jpg\;216\;216
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

\n

<
strong>COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR

\n

Oscar Olmedo\, PhD\nData Scientist\nCACI International\, Inc.

\n

Calibration for Probabilistic Classification

\n

Monday\, August 28\, 4:30-5:45\nExploratory Hall\, R
oom 3301

\n

ABSTRACT: This talk will be a
review of calibration methods for classifiers that make probabilistic pred
ictions on a scale 0 to 1. It is known that certain classification methods
\, such as Naïve Bayes or Random Forest make biased predictions that to no
t match the true posterior probabilities. By calibrating the predictions
made by classifiers the true probability of the predicted class can be det
ermined. This type of calibration can be crucial for real-world decision m
aking problems in medicine\, business\, marketing\, and finance. In this t
alk I will focus on applications in marketing.

\n

\n

Part two: Marketing yourself for future careers outside
of academia

\n

It is known that the number of job
s in academia is not rising as fast as the number of PhD’s graduating. Cur
rently a new career option is available to these PhDs\, the Data Scientist
. But how does one make the transition out of academia to this hot new fie
ld? I will discuss strategies for marketing yourself as well as tools nece
ssary to be successful in your transition.

\n

\n

Dr. Oscar Olmedo is an alumnus of George Mason University who studied
physics in undergrad (2004)\, Computational Sciences and Informatics Mast
ers (2007)\, and Computational Sciences and Informatics PhD (2011) with a
concentration in solar physics under Dr. Jie Zhang. After graduating in 20
11\, Dr. Olmedo went on to NRL as an NRC fellow for two years\, and briefl
y worked at NASA Goddard for a few months in 2013 before moving to Syntasa
\, a startup focusing on ecommerce/marketing analytics. In 2015\, he moved
to CACI to work on cyber security research as a DARPA contractor.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3943@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES:
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nWilliam G. Kennedy
\, Research Assistant Professor\nCenter for Social Complexity\nCharacteriz
ing the reaction of the population of NYC to a nuclear WMD\nFriday\, Septe
mber 1\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Resear
ch Hall\nABSTRACT: This talk will review the status of our multi-year pro
ject to characterize the reaction of the population of a US megacity to a
nuclear WMD event. Our approach is to develop an agent-based model of the
New York City area\, with agents representing each of the 20-25 million pe
ople\, and establish a baseline of normal behaviors before exploring the p
opulation’s reactions to small (5-10Kt) nuclear weapon explosions. In our
first year\, we explored understanding a large population’s reaction to a
nuclear WMD event with four major activities: (1) reviewing existing socia
l theories and reports of disaster behavior\, (2) collecting data and mode
ling the infrastructure of a mega-city and surrounding region\, (3) genera
ting synthetic population\, and (4) developing an agent-based model of all
the individuals in the region. The review of social science theories and
data on individual/group behavior during disasters led to the publication
of a case study (the Flint River drinking water crisis) and preparation of
two review papers. For the New York City mega-city and surrounding area\,
we collected spatial\, demographic\, and workforce data from several sour
ces and devised methods and algorithms to make the data useful for our sim
ulation. Using Python\, we processed road data and created one connected n
etwork forming the transportation layer of the model. Using demographic da
ta and our own heuristics\, again in Python\, we synthesized individuals\,
their households\, their associated schools and workplaces and finally th
eir social networks. Other datasets were utilized so that children attend
nearby schools or daycare constrained with actual capacities and people ar
e employed in workplaces located nearby matching workforce data. Finally\,
we began modeling individuals’ movement in three counties\, two rural cou
nties and one in the heart of Manhattan. I will start with a discussion of
the effects of a nuclear WMD event and then discuss the details our work
and our future plans.
DTSTART;TZID=America/New_York:20170901T150000
DTEND;TZID=America/New_York:20170901T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Characterizing the reaction of the
population of NYC to a nuclear WMD – William Kennedy
URL:http://cos.gmu.edu/cds/event/computational-social-science-tba-william-k
ennedy/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

COMPUTATIONAL SOCIAL SCIENCE FRI
DAY SEMINAR

\n

William G. Ke
nnedy\, Research Assistant Professor\nCenter for Social Complexity
p>\n

Characterizing the reaction of t
he population of NYC to a nuclear WMD

\n

Friday\, September 1\, 3:00 p.m.\nCenter for Social Complexi
ty Suite\n3rd Floor\, Research Hall

\n

ABSTRACT: This talk wil
l review the status of our multi-year project to characterize the reaction
of the population of a US megacity to a nuclear WMD event. Our approach i
s to develop an agent-based model of the New York City area\, with agents
representing each of the 20-25 million people\, and establish a baseline o
f normal behaviors before exploring the population’s reactions to small (5
-10Kt) nuclear weapon explosions. In our first year\, we explored understa
nding a large population’s reaction to a nuclear WMD event with four major
activities: (1) reviewing existing social theories and reports of disaste
r behavior\, (2) collecting data and modeling the infrastructure of a mega
-city and surrounding region\, (3) generating synthetic population\, and (
4) developing an agent-based model of all the individuals in the region. T
he review of social science theories and data on individual/group behavior
during disasters led to the publication of a case study (the Flint River
drinking water crisis) and preparation of two review papers. For the New Y
ork City mega-city and surrounding area\, we collected spatial\, demograph
ic\, and workforce data from several sources and devised methods and algor
ithms to make the data useful for our simulation. Using Python\, we proces
sed road data and created one connected network forming the transportation
layer of the model. Using demographic data and our own heuristics\, again
in Python\, we synthesized individuals\, their households\, their associa
ted schools and workplaces and finally their social networks. Other datase
ts were utilized so that children attend nearby schools or daycare constra
ined with actual capacities and people are employed in workplaces located
nearby matching workforce data. Finally\, we began modeling individuals’ m
ovement in three counties\, two rural counties and one in the heart of Man
hattan. I will start with a discussion of the effects of a nuclear WMD eve
nt and then discuss the details our work and our future plans.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4121@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 7039933785\; jmarr2@masonlive.gmu.edu\; https://cos.g
mu.edu/cds/
DESCRIPTION: \n \n\n \nCOMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR\nCla
udio Cioffi-Revilla\, Professor\nComputational Social Science\nDepartment
of Computational and Data Sciences\nDirector\, Center for Social Complexit
y\nGeorge Mason University\nComputational Modeling of Terrorism\nMonday\,
September 11\, 4:30-5:45\nExploratory Hall\, Room 3301\n \nComputational s
ocial scientists have investigated terrorism for decades\, but only recent
ly has the field advanced to creating the first testable formal theories.
This talk will review some important background and present recent advance
s in agent-based modeling of terrorism\, based on radicalization theory an
d research. Enduring challenges will also be covered\, as opportunities fo
r research projects\, theses\, and dissertations.\nDr. Cioffi-Revilla is a
Professor of Computational Social Science\, founding and former Chair of
the Department of Computational Social Science\, and founding and current
Director of the Mason Center for Social Complexity at George Mason Univers
ity. He holds two doctoral degrees in Political Science and International
Relations and his areas of special interest include quantitative\, mathema
tical\, and simulation models applied to complex human and social systems.
DTSTART;TZID=America/New_York:20170911T163000
DTEND;TZID=America/New_York:20170911T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Computational Mod
eling of Terrorism – Cioffi-Revilla
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-computational-modeling-of-terrorism-cioffi-revilla/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
25/2017/09/claudio.png\;216\;275\,medium\;http://cos.gmu.edu/cds/wp-conten
t/uploads/sites/25/2017/09/claudio.png\;216\;275\,large\;http://cos.gmu.ed
u/cds/wp-content/uploads/sites/25/2017/09/claudio.png\;216\;275\,full\;htt
p://cos.gmu.edu/cds/wp-content/uploads/sites/25/2017/09/claudio.png\;216\;
275
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

\n

\n

\n

\n

COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR

\n

Claudio Cioffi-Revilla\, Professor\n
Computational Social Science\nDepartment of Computational and Data S
ciences\nDirector\, Center for Social Complexity\nGeorge Mason
University

\n

Computational Model
ing of Terrorism

\n

Monday\, Sept
ember 11\, 4:30-5:45\nExploratory Hall\, Room 3301

\n

\n

Computational social scientists have investigated terrorism for decades\,
but only recently has the field advanced to creating the first testable f
ormal theories. This talk will review some important background and presen
t recent advances in agent-based modeling of terrorism\, based on radicali
zation theory and research. Enduring challenges will also be covered\, as
opportunities for research projects\, theses\, and dissertations.

\n

Dr. Cioffi-Revilla is a Professor of Computational Social Science\, foundi
ng and former Chair of the Department of Computational Social Science\, an
d founding and current Director of the Mason Center for Social Complexity
at George Mason University. He holds two doctoral degrees in Political Sci
ence and International Relations and his areas of special interest include
quantitative\, mathematical\, and simulation models applied to complex hu
man and social systems.

\n

<
!-- AddThis Share Buttons generic via filter on the_content -->
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4119@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nMatthew Oldham\, P
hD Student\nComputational Social Science Program\nDepartment of Computatio
nal and Data Sciences\nGeorge Mason University\n The Quest for Living Beta
: Investigating the Implication of Shareholder Networks\nFriday\, Septembe
r 15\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Researc
h Hall\nThe behavior of financial markets has\, and continues\, to frustra
te investors and academics. With the advent of new approaches\, including
complex systems and network analysis\, the search for an explanation as to
how and why markets behavior as they do has greatly expanded\, and moved
away from the tradition neoclassical approaches that have been beholden to
the Efficient Market Hypothesis.\nThe complex system approach utilizes a
number of a concepts in an attempt to understand stock market returns incl
uding\; imitation\, herding\, self-organized co-operativity\, and positive
feedbacks\, with many of these features captured by network analysis. In
addition\, with the meteoric rises of network science has come the realiza
tion that the behavior of a system can vary greatly depending on the netwo
rk structure (the topology) of a system\, thus providing further impetus f
or the use of network analysis in terms of financial markets.\nMy presenta
tion will detail my recent research of the US Institutional shareholder ne
tworks over the period of 2007-10\, a period which includes the beginning
of the Global Financial Crisis. The research utilized an extensive dataset
provided from the Thomson Reuters 13f database\, to undertake a temporal
analysis of the networks formed between US institutional investors and the
stocks in the S&P 500. The analysis makes use of both projected and bipar
tite networks and uncovers numerous insights regarding relationships betwe
en the market in general\, stocks and their shareholders. In addition\, I
will illustrate how the findings can be used in conjunction with an agent-
based model to uncover the workings of the stock market.
DTSTART;TZID=America/New_York:20170915T150000
DTEND;TZID=America/New_York:20170915T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – The Quest for Living Beta: Investiga
ting the Implication of Shareholder Networks – Oldham
URL:http://cos.gmu.edu/cds/event/computational-social-science-the-quest-for
-living-beta-investigating-the-implication-of-shareholder-networks-oldham/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

The behavior of financial markets has\, and
continues\, to frustrate investors and academics. With the advent of new
approaches\, including complex systems and network analysis\, the search f
or an explanation as to how and why markets behavior as they do has greatl
y expanded\, and moved away from the tradition neoclassical approaches tha
t have been beholden to the Efficient Market Hypothesis.

\n

The compl
ex system approach utilizes a number of a concepts in an attempt to unders
tand stock market returns including\; imitation\, herding\, self-organized
co-operativity\, and positive feedbacks\, with many of these features cap
tured by network analysis. In addition\, with the meteoric rises of networ
k science has come the realization that the behavior of a system can vary
greatly depending on the network structure (the topology) of a system\, th
us providing further impetus for the use of network analysis in terms of f
inancial markets.

\n

My presentation will detail my recent research of the US Institutional
shareholder networks over the period of 2007-10\, a period which includes
the beginning of the Global Financial Crisis. The research utilized an ex
tensive dataset provided from the Thomson Reuters 13f database\, to undert
ake a temporal analysis of the networks formed between US institutional in
vestors and the stocks in the S&P 500. The analysis makes use of both proj
ected and bipartite networks and uncovers numerous insights regarding rela
tionships between the market in general\, stocks and their shareholders. I
n addition\, I will illustrate how the findings can be used in conjunction
with an agent-based model to uncover the workings of the stock market.

Dr. Simpson is a seasoned researcher in the areas of machine learni
ng\, deep learning\, and cybersecurity. He has served as Principal Investi
gator and Data Scientist on several DARPA programs with a focus on machine
learning applications to data fusion\, prediction\, and unsupervised anom
aly detection problems. He holds a Ph.D. in Electrical Engineering from No
rth Carolina State University where he developed novel receivers and algor
ithms for undersea communications.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4170@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars\,Seminar
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nAndrew Crooks\, As
sociate Professor\nComputational Social Science\nDepartment of Computation
al and Data Sciences\nGeorge Mason University\nABM for Simulating Spatial
Systems: How are we doing?\nFriday\, September 22\, 3:00 p.m.\nCenter for
Social Complexity Suite\n3rd Floor\, Research Hall\n \nWhile great advanc
es in modeling have been made\, one of the greatest challenges we face is
that of understanding human behavior and how people perceive and behave in
physical spaces. Can new sources of data (i.e. “big data”) be used to exp
lore the connections between people and places? \nIn this presentation\,
I will review the current state of art of modeling geographical systems.
I will highlight the challenges and opportunities through a series of exa
mples that new data can be used to better understand and simulate how indi
viduals behave within geographical systems.
DTSTART;TZID=America/New_York:20170922T150000
DTEND;TZID=America/New_York:20170922T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – ABM for Simulating Spatial S
ystems: How are we doing? – Crooks
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-abm-f
or-simulating-spatial-systems-how-are-we-doing-crooks/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

While great advances in modeling have been made\, one of the
greatest challenges we face is that of understanding human behavior and h
ow people perceive and behave in physical spaces. Can new sources of data
(i.e. “big data”) be used to explore the connections between people and pl
aces?

\n

In this presentation\,
I will review the current state of art of modeling geographical systems.
I will highlight the challenges and opportunities through a series of exa
mples that new data can be used to better understand and simulate how indi
viduals behave within geographical systems.

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3973@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 703-993-5017\; jmarr2@masonlive.gmu.edu\; https://cos
.gmu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR \n \n\nLauren D
eason\, PhD\nLead Data Scientist\nPunch Cyber Analytics Group\n\nDetecting
Automated and Coordinated Activity in Cyber Logs at Scale\n\nMonday\, Sep
tember 25\, 4:30-5:45\nExploratory Hall\, Room 3301\nABSTRACT: This presen
tation will detail novel analytic methods for processing time series data
at scale using techniques drawn from digital signal processing and documen
t matching. These methods can be applied to detect coordinated and automat
ed cyber activity\, to match patterns present in time series data\, and to
fuse together disparate datasets.\nDr. Deason holds a BS in Applied Mathe
matics from the University of Virginia\, a MA in Mathematics with an empha
sis in Real Analysis and Probability Theory from UC Berkeley\, and a PhD i
n Economics from the University of Maryland\, College Park. Dr. Deason has
10 years of experience in mathematical modeling and data science\, spanni
ng employment as a Professor of Mathematics\, an Economist\, and a Data Sc
ientist. Dr. Deason’s past experience includes developing dynamic stochast
ic models within a game theoretic framework to explore the effects of trad
e policy uncertainty as well as estimating empirical models to explain var
ious phenomena. More recently\, Dr. Deason has developed multiple algorith
ms for detecting and classifying periodic and coordinated behavior in a va
riety of contexts on large data sets as part of DARPA’s Network Defense Pr
ogram.\nDEASON_gmu_pres\n
DTSTART;TZID=America/New_York:20170925T163000
DTEND;TZID=America/New_York:20170925T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Detecting Automat
ed and Coordinated Activity in Cyber Logs at Scale – Deason
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-detecting-automated-and-coordinated-activity-deason/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
25/2017/08/DEASON.jpg\;218\;254\,medium\;http://cos.gmu.edu/cds/wp-content
/uploads/sites/25/2017/08/DEASON.jpg\;218\;254\,large\;http://cos.gmu.edu/
cds/wp-content/uploads/sites/25/2017/08/DEASON.jpg\;218\;254\,full\;http:/
/cos.gmu.edu/cds/wp-content/uploads/sites/25/2017/08/DEASON.jpg\;218\;254
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

ABSTRACT: This presentatio
n will detail novel analytic methods for processing time series data at sc
ale using techniques drawn from digital signal processing and document mat
ching. These methods can be applied to detect coordinated and automated cy
ber activity\, to match patterns present in time series data\, and to fuse
together disparate datasets.

\n

Dr. Deason holds a BS in Applied Mat
hematics from the University of Virginia\, a MA in Mathematics with an emp
hasis in Real Analysis and Probability Theory from UC Berkeley\, and a PhD
in Economics from the University of Maryland\, College Park. Dr. Deason h
as 10 years of experience in mathematical modeling and data science\, span
ning employment as a Professor of Mathematics\, an Economist\, and a Data
Scientist. Dr. Deason’s past experience includes developing dynamic stocha
stic models within a game theoretic framework to explore the effects of tr
ade policy uncertainty as well as estimating empirical models to explain v
arious phenomena. More recently\, Dr. Deason has developed multiple algori
thms for detecting and classifying periodic and coordinated behavior in a
variety of contexts on large data sets as part of DARPA’s Network Defense
Program.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3986@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Karen Underwood\; 703-993-9298\; kunderwo@gmu.edu\; https://cos.gmu
.edu/cds/
DESCRIPTION:Computational Social Science\nRobert Axtell\, PhD\nComputationa
l Social Science Program\, Department of Computational and Data Sciences\,
\nCollege of Science\nDepartment of Economics\, College of Humanities and
Social Sciences\nKrasnow Institute for Advanced Study\nGeorge Mason Univer
sity\nCo-Director\nComputational Public Policy Lab\nKrasnow Institute for
Advanced Study and Schar School of Policy and Government\nExternal Profess
or\, Santa Fe Institute (santafe.edu)\nExternal Faculty\, Northwestern Ins
titute on Complex Systems (nico.northwestern.edu)\nScientific Advisory Cou
ncil\, Waterloo Institute for Complexity and Innovation (wici.ca)\nGetting
Younger by Growing Older: U.S. Firms Gain Longevity as they Age\nFriday\,
September 29\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\
, Research Hall\n \nAbstract: Using data on the entire population of Amer
ican firms I will first show that the distribution of firm ages is approxi
mately stationary\, with small ‘defects’ arising at the start of last deca
de’s Financial Crisis now propagating through the distribution. From these
data I will derive the distribution of U.S. firm lifetimes and demonstrat
e that it has a specific structure that conforms to economists’ intuitions
about new (and small) firms having higher failure probabilities than olde
r (and larger) firms. I will then demonstrate that the large body of stati
stical theory on ‘survival analysis’ is directly applicable to firms. Spec
ifically\, I will focus on firm hazard functions and empirically show that
for the U.S. this is a power law over a wide range of ages\, ostensibly a
new finding\, declining with age. This permits computation of the expecte
d remaining lifetime of firms as a function of their age\, an INCREASING f
unction\, implying that American firms gain longevity as they get older\,
a very non-biological type of aging. Conditioning on firm size produces fu
rther results. Specifically\, using the Cox ‘proportional hazards’ specifi
cation\, the reduction in failure probability associated with larger size
is quantified. At the end I will demonstrate that an ABM of firm dynamics
can be calibrated to reproduce all of these features of U.S. firms.\nRob A
xtell earned an interdisciplinary Ph.D. degree at Carnegie Mellon Universi
ty\, where he studied computing\, social science\, and public policy. His
teaching and research involves computational and mathematical modeling of
social and economic processes. Specifically\, he works at the intersection
of multi-agent systems computer science and the social sciences\, buildin
g so-called agent-based models for a variety of market and non-market phen
omena.\nHis research has been published in the leading scientific journals
\, including Science and the Proceedings of the National Academy of Scienc
es\, USA\, and reprised in Nature\, and has appeared in top disciplinary j
ournals (e.g.\, American Economic Review\, Computational and Mathematical
Organization Theory\, Economic Journal)\, in general interest journals (e.
g.\, PLOS One) and in specialty journals (e.g.\, Journal of Regulatory Eco
nomics\, Technology Forecasting and Social Change.) His research has been
supported by American philanthropies (e.g.\, John D. and Catherine T. MacA
rthur Foundation\, Institute for New Economic Thinking) and government org
anizations (e.g.\, National Science Foundation\, Department of Defense\, S
mall Business Administration\, Office of Naval Research\, Environmental Pr
otection Agency). Stories about his research have appeared in major magazi
nes (e.g.\, Economist\, Atlantic Monthly\, Scientific American\, New Yorke
r\, Discover\, Wired\, New Scientist\, Technology Review\, Forbes\, Harvar
d Business Review\, Science News\, Chronicle of Higher Education\, Byte\,
Le Temps Strategique) and newspapers (e.g.\, Wall St. Journal\, Washington
Post\, Los Angeles Times\, Boston Globe\, Detroit Free Press\, Financial
Times). He is co-author of Growing Artificial Societies: Social Science fr
om the Bottom Up (MIT Press) with J.M. Epstein\, widely cited as an exampl
e of how to apply modern computing to the analysis of social and economic
phenomena.
DTSTART;TZID=America/New_York:20170929T150000
DTEND;TZID=America/New_York:20170929T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Ages and Lifetimes of U.S. F
irms – Axtell
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-ages-and-lifetimes-of-u-s-firms-axtell/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
25/2015/06/axtell.jpg\;166\;188\,medium\;http://cos.gmu.edu/cds/wp-content
/uploads/sites/25/2015/06/axtell.jpg\;166\;188\,large\;http://cos.gmu.edu/
cds/wp-content/uploads/sites/25/2015/06/axtell.jpg\;166\;188\,full\;http:/
/cos.gmu.edu/cds/wp-content/uploads/sites/25/2015/06/axtell.jpg\;166\;188
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

\n

Computational
Social Science

\n

Robert Ax
tell\, PhD\nComputational Social Science Program\, Department of Com
putational and Data Sciences\,\nCollege of Science\nDepartment
of Economics\, College of Humanities and Social Sciences\nKrasnow I
nstitute for Advanced Study\nGeorge Mason University

\n

Co-Director\nComputational Public Policy Lab\nKrasnow Institute for Advanced Study and Schar School of Policy and Go
vernment

Getting Young
er by Growing Older: U.S. Firms Gain Longevity as they Age

\n

Friday\, September 29\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall

\n

\n

Abstract: Using data on the entire popul
ation of American firms I will first show that the distribution of firm ag
es is approximately stationary\, with small ‘defects’ arising at the start
of last decade’s Financial Crisis now propagating through the distributio
n. From these data I will derive the distribution of U.S. firm lifetimes a
nd demonstrate that it has a specific structure that conforms to economist
s’ intuitions about new (and small) firms having higher failure probabilit
ies than older (and larger) firms. I will then demonstrate that the large
body of statistical theory on ‘survival analysis’ is directly applicable t
o firms. Specifically\, I will focus on firm hazard functions and empirica
lly show that for the U.S. this is a power law over a wide range of ages\,
ostensibly a new finding\, declining with age. This permits computation o
f the expected remaining lifetime of firms as a function of their age\, an
INCREASING function\, implying that American firms gain longevity as they
get older\, a very non-biological type of aging. Conditioning on firm siz
e produces further results. Specifically\, using the Cox ‘proportional haz
ards’ specification\, the reduction in failure probability associated with
larger size is quantified. At the end I will demonstrate that an ABM of f
irm dynamics can be calibrated to reproduce all of these features of U.S.
firms.

\n

Rob Axtell earned an interdisciplinary Ph.D. degree
at Carnegie Mellon University\, where he studied computing\, social scienc
e\, and public policy. His teaching and research involves computational an
d mathematical modeling of social and economic processes. Specifically\, h
e works at the intersection of multi-agent systems computer science and th
e social sciences\, building so-called agent-based models for a variety of
market and non-market phenomena.

\n

His research has been published
in the leading scientific journals\, including Science and the P
roceedings of the National Academy of Sciences\, USA\, and repr
ised in Nature\, and has appeared in top disciplinary journals (e.g
.\, American Economic Review\, Computational and Mathematical Or
ganization Theory\, Economic Journal)\, in general interest jou
rnals (e.g.\, PLOS One) and in specialty journals (e.g.\, Journa
l of Regulatory Economics\, Technology Forecasting and Social Chang
e.) His research has been supported by American philanthropies (e.g.\,
John D. and Catherine T. MacArthur Foundation\, Institute for New Economi
c Thinking) and government organizations (e.g.\, National Science Foundati
on\, Department of Defense\, Small Business Administration\, Office of Nav
al Research\, Environmental Protection Agency). Stories about his research
have appeared in major magazines (e.g.\, Economist\, Atlantic M
onthly\, Scientific American\, New Yorker\, Discover<
/i>\, Wired\, New Scientist\, Technology Review\,
Forbes\, Harvard Business Review\, Science News\, Chr
onicle of Higher Education\, Byte\, Le Temps Strategique
) and newspapers (e.g.\, Wall St. Journal\, Washington Post\
, Los Angeles Times\, Boston Globe\, Detroit Free Press
i>\, Financial Times). He is co-author of Growing Artificial Soc
ieties: Social Science from the Bottom Up (MIT Press) with J.M. Epstei
n\, widely cited as an example of how to apply modern computing to the ana
lysis of social and economic phenomena.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3971@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 703-993-5017\; jmarr2@masonlive.gmu.edu\; https://cos
.gmu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR \nKirk Borne\,
PhD\nPrincipal Data Scientist and an Executive Advisor\nBooz Allen Hamilto
n\nSmart Data for Smart Science\nMonday\, October 2\, 4:30-5:45\nExplorat
ory Hall\, Room 3301\nABSTRACT: Smart data are essential when faced with
massive-scale data collections. “Smart” refers to data that are tagged or
indexed with meaning-filled metadata that carry information about the sema
ntic meaning of the data\, its applications\, use cases\, content\, contex
t\, and more. Such meta-tags enable efficient and effective discovery\, de
scription\, and delivery of the right data at the right time\, both to hum
ans and to automatic processes.\nDr. Borne advises and consults with numer
ous organizations\, agencies\, and partners in the use of data and analyti
cs for discovery\, decision support\, and innovation. Previously\, he was
Professor at George Mason University (GMU) for 12 years in the CSI and CDS
programs\, where he did research\, taught\, and advised students in data
science. Prior to that\, Dr. Borne spent nearly 20 years supporting data s
ystems activities on NASA space science research programs\, including a ro
le as NASA’s Data Archive Project Scientist for the Hubble Space Telescope
.\nRecently\, Dr. Borne was ranked #2 worldwide among all Big Data experts
to follow. http://ipfconline.fr/blog/2017/05/22/fine-list-of-50-top-worl
d-big-data-experts-to-follow-in-2017-with-moz-social-score/
DTSTART;TZID=America/New_York:20171002T163000
DTEND;TZID=America/New_York:20171002T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Smart Data for Sm
art Science – Borne
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-smart-data-for-smart-science-borne/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
25/2017/08/IMG_8429.jpg\;216\;216\,medium\;http://cos.gmu.edu/cds/wp-conte
nt/uploads/sites/25/2017/08/IMG_8429.jpg\;216\;216\,large\;http://cos.gmu.
edu/cds/wp-content/uploads/sites/25/2017/08/IMG_8429.jpg\;216\;216\,full\;
http://cos.gmu.edu/cds/wp-content/uploads/sites/25/2017/08/IMG_8429.jpg\;2
16\;216
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

ABSTRACT: Smart
data are essential when faced with massive-scale data collections. “Smart
” refers to data that are tagged or indexed with meaning-filled metadata t
hat carry information about the semantic meaning of the data\, its applica
tions\, use cases\, content\, context\, and more. Such meta-tags enable ef
ficient and effective discovery\, description\, and delivery of the right
data at the right time\, both to humans and to automatic processes.

\n<
p>Dr. Borne advises and consults with numerous organizations\, agencies\,
and partners in the use of data and analytics for discovery\, decision sup
port\, and innovation. Previously\, he was Professor at George Mason Unive
rsity (GMU) for 12 years in the CSI and CDS programs\, where he did resear
ch\, taught\, and advised students in data science. Prior to that\, Dr. Bo
rne spent nearly 20 years supporting data systems activities on NASA space
science research programs\, including a role as NASA’s Data Archive Proje
ct Scientist for the Hubble Space Telescope.\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3939@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nXiaoyi Yuan\, PhD
Student\nComputational Social Science Program\nDepartment of Computational
and Data Sciences\nGeorge Mason University\nQuantifying the Social Debate
s of Anti-Vaccination on Twitter\nFriday\, October 6\, 3:00 p.m.\nCenter f
or Social Complexity Suite\n3rd Floor\, Research Hall\n \nMeasles is one o
f the leading causes of death among young children. In many developed coun
tries with high measles\, mumps\, and rubella (MMR) vaccine coverage\, mea
sles outbreaks still happen each year. Social media has been one of the do
minant information sources to gain vaccination knowledge and thus has also
been the focus of the “anti-vaccine movement”. This talk is about two of
my recent research projects on this topic. The first one will be introduce
d briefly\, which is an agent-based model demonstrating how a small amount
of online anti-vaccine sentiment could have the power of increasing the p
robability of measles outbreaks significantly. This research inspired me t
o investigate details of communicative pattern of “anti-vacciners” by anal
yzing a large twitter dataset (660892 tweets) after the California Disneyl
and measles outbreak in 2015. This second research has two main parts: fir
st\, in order to identify “anti-vacciners”\, I used supervised learning to
label each tweet as either positive\, neutral\, or negative opinion towar
ds vaccination. The linear support vector machine model shows good perform
ance on this dataset with an accuracy score of 72% on test data. Second\,
Louvain’s method for community detection of the retweet network shows the
common pattern of social media communities\; i.e.\, overall fragmented but
with a few large communities. By investigating the opinion distribution i
n big communities\, however\, I discovered that they are highly overlapped
\, especially within “anti-vacciners”\, meaning that they have more freque
nt communication within their own opinion group than with others. What’s u
seful for health communication strategies is to look further into the brok
ers–those who stand between two or more communities. At the end of the tal
k\, I will address details of analyzing the brokerage as well.
DTSTART;TZID=America/New_York:20171006T150000
DTEND;TZID=America/New_York:20171006T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Quantifying the Social Debat
es of Anti-Vaccination on Twitter – Yuan
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-tba-x
iaoyi-yuan/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Measles is one of the leading
causes of death among young children. In many developed countries with hig
h measles\, mumps\, and rubella (MMR) vaccine coverage\, measles outbreaks
still happen each year. Social media has been one of the dominant informa
tion sources to gain vaccination knowledge and thus has also been the focu
s of the “anti-vaccine movement”. This talk is about two of my recent rese
arch projects on this topic. The first one will be introduced briefly\, wh
ich is an agent-based model demonstrating how a small amount of online ant
i-vaccine sentiment could have the power of increasing the probability of
measles outbreaks significantly. This research inspired me to investigate
details of communicative pattern of “anti-vacciners” by analyzing a large
twitter dataset (660892 tweets) after the California Disneyland measles ou
tbreak in 2015. This second research has two main parts: first\, in order
to identify “anti-vacciners”\, I used supervised learning to label each tw
eet as either positive\, neutral\, or negative opinion towards vaccination
. The linear support vector machine model shows good performance on this d
ataset with an accuracy score of 72% on test data. Second\, Louvain’s meth
od for community detection of the retweet network shows the common pattern
of social media communities\; i.e.\, overall fragmented but with a few la
rge communities. By investigating the opinion distribution in big communit
ies\, however\, I discovered that they are highly overlapped\, especially
within “anti-vacciners”\, meaning that they have more frequent communicati
on within their own opinion group than with others. What’s useful for heal
th communication strategies is to look further into the brokers–those who
stand between two or more communities. At the end of the talk\, I will add
ress details of analyzing the brokerage as well.

ABSTRACT: I will present a variety of atypical use cases and applica
tions (in science and in business) of some typical textbook machine learni
ng algorithms\, including regression\, clustering analysis\, association m
ining\, time series analysis\, and network analysis.

\n

Dr. Borne adv
ises and consults with numerous organizations\, agencies\, and partners in
the use of data and analytics for discovery\, decision support\, and inno
vation. Previously\, he was Professor at George Mason University (GMU) for
12 years in the CSI and CDS programs\, where he did research\, taught\, a
nd advised students in data science. Prior to that\, Dr. Borne spent nearl
y 20 years supporting data systems activities on NASA space science resear
ch programs\, including a role as NASA’s Data Archive Project Scientist fo
r the Hubble Space Telescope.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4213@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:defense
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:Notice and Invitation\nOral Defense of Doctoral Dissertation\nD
octor of Philosophy in Computational Sciences and Informatics\nDepartment
of Computational and Data Sciences\nCollege of Science\nGeorge Mason Unive
rsity\nDaniel Pulido\nBachelor of Science\, Boston University\, 1998\nMast
er of Science\, Worcester Polytechnic Institute\, 2003\nSelf-Similar Spin
Images for Point Cloud Matching\n\nFriday\, October 13\, 2017\, 10:00 a.m.
\nResearch Hall\, Room 162\nAll are invited to attend.\nCommittee\nAnthony
Stefanidis\, Dissertation Director\nEstela Blaisten-Barojas\nArie Croitor
u\nJuan Cebral\nThe rapid growth of Light Detection And Ranging (Lidar) te
chnologies that collect\, process\, and disseminate 3D point clouds have a
llowed for increasingly accurate spatial modeling and analysis of the real
world. Lidar sensors can generate massive 3D point clouds of a collection
area that provide highly detailed spatial and radiometric information. Si
multaneously\, the growth of other forms of geospatial data (e.g.\, crowds
ourced Web 2.0 data) have provided researchers with a wealth of freely ava
ilable data sources that cover a variety of geographic areas. However\, co
mbining data from disparate sources requires overcoming numerous technical
challenges in order to generate products that mitigate their respective d
isadvantages and combine their advantages.\nTherefore\, this dissertation
addresses the problem of fusing two point clouds from potentially differen
t sources by considering two specific problems: scale matching and feature
matching. To address the problem of feature matching we develop a novel f
eature descriptor referred to as “Self-Similar Spin Images” which combine
the concept of local self-similarity with the descriptive power of Spin Im
ages. To address the problem of scale matching we develop a novel scale de
tection metric referred to as “Self-Similar Keyscale” which analyzes the s
elf-similarity of two point clouds to identify a characteristic scale to m
atch them. Finally\, we develop a novel change detection method as a sampl
e use case of the developed matching techniques.\n
DTSTART;TZID=America/New_York:20171013T100000
DTEND;TZID=America/New_York:20171013T120000
LOCATION:Research Hall\, Room 162
SEQUENCE:0
SUMMARY:Oral Defense of Doctoral Dissertation – Doctor of Philosophy in Com
putational Sciences and Informatics – Department of Computational and Data
Sciences – Daniel Lupido
URL:http://cos.gmu.edu/cds/event/oral-defense-of-doctoral-dissertation-doct
or-of-philosophy-in-computational-sciences-and-informatics-department-of-c
omputational-and-data-sciences-daniel-lupido/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Notice and Invitation\nOral Defense of Doctoral Dissertation\nDoctor of Philosophy in Computational Sciences and Informatics\n
Department of Computational and Data Sciences\nCollege of Science\nGeorge Mason University

The rapid growth of Light Detection
And Ranging (Lidar) technologies that collect\, process\, and disseminate
3D point clouds have allowed for increasingly accurate spatial modeling a
nd analysis of the real world. Lidar sensors can generate massive 3D point
clouds of a collection area that provide highly detailed spatial and radi
ometric information. Simultaneously\, the growth of other forms of geospat
ial data (e.g.\, crowdsourced Web 2.0 data) have provided researchers with
a wealth of freely available data sources that cover a variety of geograp
hic areas. However\, combining data from disparate sources requires overco
ming numerous technical challenges in order to generate products that miti
gate their respective disadvantages and combine their advantages.

\n

Therefore\, this dissertation addresses the problem of fusing two point cl
ouds from potentially different sources by considering two specific proble
ms: scale matching and feature matching. To address the problem of feature
matching we develop a novel feature descriptor referred to as “Self-Simil
ar Spin Images” which combine the concept of local self-similarity with th
e descriptive power of Spin Images. To address the problem of scale matchi
ng we develop a novel scale detection metric referred to as “Self-Similar
Keyscale” which analyzes the self-similarity of two point clouds to identi
fy a characteristic scale to match them. Finally\, we develop a novel chan
ge detection method as a sample use case of the developed matching techniq
ues.

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3937@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; http://cos.gmu.ed
u/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nAnnetta Burger\, P
hD Candidate\nComputational Social Science Program\nDepartment of Computa
tional and Data Sciences\nGeorge Mason University\nOrganizing Theories for
Disaster Study in Computational Social Science\nFriday\, October 13\, 3:0
0 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall\nBas
ed on evolving understandings of the complex\, interconnected characterist
ics of disasters and social adaptations we propose to organize disaster th
eories in the interdisciplinary context of complex adaptive systems (CAS).
Within this context disasters can be understood to consist of three sets
of interacting systems\, the socio-ecological system\, the system of colle
ctive\, social behavior\, and the individual actor’s cognitive system. Ong
oing dynamic forces within each set periodically build to disrupt events a
nd cause failures in the overall system. In this talk\, we will explore th
e dominant theory and frameworks in disaster studies from the perspective
of these sets\, demonstrate how they explain disasters\, and discuss how C
omputational Social Science methodologies can support theory-building. By
identifying evolving understandings and research questions co-located in d
isaster theory and CAS we find that the CAS features of heterogeneity\, fl
ows\, interacting subsystems\, emergence from self-organization and bottom
-up processes\, and adaptation and learning are integral to disaster studi
es.
DTSTART;TZID=America/New_York:20171013T150000
DTEND;TZID=America/New_York:20171013T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Organizing Theories for Disa
ster Study in Computational Social Science – Burger
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-organ
izing-theories-for-disaster-study-in-computational-social-science-annetta-
burger/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Based on evolving understandings of the complex\, interconnected charact
eristics of disasters and social adaptations we propose to organize disast
er theories in the interdisciplinary context of complex adaptive systems (
CAS). Within this context disasters can be understood to consist of three
sets of interacting systems\, the socio-ecological system\, the system of
collective\, social behavior\, and the individual actor’s cognitive system
. Ongoing dynamic forces within each set periodically build to disrupt eve
nts and cause failures in the overall system. In this talk\, we will explo
re the dominant theory and frameworks in disaster studies from the perspec
tive of these sets\, demonstrate how they explain disasters\, and discuss
how Computational Social Science methodologies can support theory-building
. By identifying evolving understandings and research questions co-located
in disaster theory and CAS we find that the CAS features of heterogeneity
\, flows\, interacting subsystems\, emergence from self-organization and b
ottom-up processes\, and adaptation and learning are integral to disaster
studies.

\n

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4102@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 703-993-5017\; jmarr2@masonlive.gmu.edu\; https://cos
.gmu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR\n\nStephen Lock
ett\, PhD\nPrincipal Scientist and Director of the Optical Microscopy and
Analysis Laboratory\nFrederick National Laboratory for Cancer Research\, N
ational Cancer Institute\nTransitioning Microscopy from 2D to 3D for Tumor
Biology and Pathology\nMonday\, October 16\, 4:30-5:45\nExploratory Hall\
, Room 3301\n \nAbstract: Discerning the 3D context of individual cells in
tissue is fundamental for understanding tissue development\, homeostasis
in healthy adult organs and tumor emergence. Determining this context\, w
hich requires 3D microscopic imaging to 100s of microns depth into tissues
\, is hampered in multiple ways: (1) penetration of fluorescent dyes deep
into tissue\, (2) absorption and scattering of light\, (3) sufficiently hi
gh speed image acquisition and (4) automated image analysis\, because data
sets are too large for human visual interpretation. Furthermore\, in the
case of tumors\, standard practice in clinical pathology is to only image
a thin slice about 5 microns thick\, which has several limitations: there
is significant inter-observer variability in the diagnosis\, the slice can
miss the tumor and determining the spatial relationships of cells to each
other is incomplete. To address these limitations\, we have evaluated ti
ssue clearing protocols\, which reduce scattering and spherical aberration
\, and we have achieved high spatial resolution imaging up 0.35 mm depth u
tilizing spinning disk confocal microscopy or lightsheet microscopy for hi
gh speed image acquisition. The image analysis tasks for 3D images are mu
ch more demanding than 2D images for the following reasons: (1) visualizat
ion of the entirety of each object (cell or cell nucleus) is not possible
in a single display\, (2) purely interactive segmentation is impractical e
ven for one object\, (3) automatic algorithms are imperfect\, and (4) in t
he case of basic research relatively few cells are imaged increasing the n
eed to analyze each cell as accurately as possible. Consequently\, we are
working on tools that merge automatic segmentation algorithms\, computer
vision and human annotation for delineating objects in 3D images. We have
developed a graphcut-based algorithm for finding the globally-optimal sur
face delineating each manually seeded nucleus. The algorithm is restricte
d to point convex objects\, which is generally satisfactory for nuclei\, b
ut not for entire cells that can have arbitrary shapes. For whole cell se
gmentation in 3D\, our approach is slice-by-slice based. In the first 2D
image slice (approximately through the center of the cell)\, the user draw
s an approximate 2D border and the border is automatically optimized using
active contour modeling. This optimized border then serves as the approx
imate border for adjacent slices\, which are in turn automatically optimiz
ed. The method is accurate except where the cell surface is in the plane
of the slice\, so a next step is to facilitate segmenting each cell in dif
ferent orientations. We are starting to utilize this technology to unde
rstand the complex interplay between cancer and normal cells\, particularl
y immune system cells and supporting mesenchyme leading to tumor growth or
regression in the case of treatment. The research was funded by NCI Cont
ract No. HHSN261200800001E and supported in part by the Intramural Researc
h Program of the National Institutes of Health\, National Cancer Institute
\, Center for Cancer Research.\nStephen Lockett received the Ph.D. degree
from the Department of Medicine\, Birmingham University\, England. He has
published over 120 research papers and has received several international
awards. His research interests include fluorescence microscopy and the d
evelopment of analysis software for extracting quantitative information fr
om images.
DTSTART;TZID=America/New_York:20171016T163000
DTEND;TZID=America/New_York:20171016T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Transitioning Mic
roscopy 2D to 3D for Tumor Biology and Pathology – Lockett
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-transitioning-microscopy-2d-to-3d-for-tumor-biology-and-pathology-lo
ckett/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
25/2017/08/Stephen-Lockett_070505.jpg\;141\;235\,medium\;http://cos.gmu.ed
u/cds/wp-content/uploads/sites/25/2017/08/Stephen-Lockett_070505.jpg\;141\
;235\,large\;http://cos.gmu.edu/cds/wp-content/uploads/sites/25/2017/08/St
ephen-Lockett_070505.jpg\;141\;235\,full\;http://cos.gmu.edu/cds/wp-conten
t/uploads/sites/25/2017/08/Stephen-Lockett_070505.jpg\;141\;235
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Abstract: Discerning the
3D context of individual cells in tissue is fundamental for understanding
tissue development\, homeostasis in healthy adult organs and tumor emerge
nce. Determining this context\, which requires 3D microscopic imaging to
100s of microns depth into tissues\, is hampered in multiple ways: (1) pen
etration of fluorescent dyes deep into tissue\, (2) absorption and scatter
ing of light\, (3) sufficiently high speed image acquisition and (4) autom
ated image analysis\, because datasets are too large for human visual inte
rpretation. Furthermore\, in the case of tumors\, standard practice in cl
inical pathology is to only image a thin slice about 5 microns thick\, whi
ch has several limitations: there is significant inter-observer variabilit
y in the diagnosis\, the slice can miss the tumor and determining the spat
ial relationships of cells to each other is incomplete. To address these
limitations\, we have evaluated tissue clearing protocols\, which reduce s
cattering and spherical aberration\, and we have achieved high spatial res
olution imaging up 0.35 mm depth utilizing spinning disk confocal microsco
py or lightsheet microscopy for high speed image acquisition. The image a
nalysis tasks for 3D images are much more demanding than 2D images for the
following reasons: (1) visualization of the entirety of each object (cell
or cell nucleus) is not possible in a single display\, (2) purely interac
tive segmentation is impractical even for one object\, (3) automatic algor
ithms are imperfect\, and (4) in the case of basic research relatively few
cells are imaged increasing the need to analyze each cell as accurately a
s possible. Consequently\, we are working on tools that merge automatic s
egmentation algorithms\, computer vision and human annotation for delineat
ing objects in 3D images. We have developed a graphcut-based algorithm fo
r finding the globally-optimal surface delineating each manually seeded nu
cleus. The algorithm is restricted to point convex objects\, which is gen
erally satisfactory for nuclei\, but not for entire cells that can have ar
bitrary shapes. For whole cell segmentation in 3D\, our approach is slice
-by-slice based. In the first 2D image slice (approximately through the c
enter of the cell)\, the user draws an approximate 2D border and the borde
r is automatically optimized using active contour modeling. This optimize
d border then serves as the approximate border for adjacent slices\, which
are in turn automatically optimized. The method is accurate except where
the cell surface is in the plane of the slice\, so a next step is to faci
litate segmenting each cell in different orientations. We are starting
to utilize this technology to understand the complex interplay between can
cer and normal cells\, particularly immune system cells and supporting mes
enchyme leading to tumor growth or regression in the case of treatment. T
he research was funded by NCI Contract No. HHSN261200800001E and supported
in part by the Intramural Research Program of the National Institutes of
Health\, National Cancer Institute\, Center for Cancer Research.

\n

S
tephen Lockett received the Ph.D. degree from the Department of Medicine\,
Birmingham University\, England. He has published over 120 research pape
rs and has received several international awards. His research interests
include fluorescence microscopy and the development of analysis software f
or extracting quantitative information from images.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4256@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars\,Seminar
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nPeter Revay\, Ph.D
. Candidate\nComputational Social Science Program\nDepartment of Computati
onal and Data Sciences\nGeorge Mason University\nModeling the Co-Evolution
of Culture\, Signs and Network Structure: Theory and Applications\nFriday
\, October 20\,3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\,
Research Hall\nABSTRACT: I focus on the drivers of diffusion and adoption
of cultural traits\, such as values\, beliefs\, and behaviors. I adopt an
evolutionary view of cultural dynamics. I use concepts from dual-inherita
nce theories of cultural evolution to develop and test an agent-based mode
l capable of simulating the changing distributions of cultural traits in a
large population of actors over the course of prolonged periods of time.
Particularly\, I pay close attention to the mechanisms of indirectly biase
d transmission of traits and guided variation\, which are both hypothesize
d to be significant aspects of cultural dynamics. Indirectly biased transm
ission consists of the adoption of specific trait variants on the basis of
possession of initially unrelated external markers. Guided variation is t
hen individual adaptation driven by self-exploration.\nFurthermore\, I mak
e use of large publicly available datasets to validate my models. The firs
t one of these is the database of bill co-authorship in the U.S. House of
Representatives from 1973 to 2008. The other is a comprehensive dataset of
scientific co-authorship in various disciplines stretching back for over
a century.\nThe results show that cultural evolution models based on indir
ectly biased transmission and guided variation are suitable to explaining
the dynamics of various complex social networks.
DTSTART;TZID=America/New_York:20171020T150000
DTEND;TZID=America/New_York:20171020T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Modeling the Co-Evolution of Culture
\, Signs and Network Structure: Theory and Applications – Revay
URL:http://cos.gmu.edu/cds/event/computational-social-science-modeling-the-
co-evolution-of-culture-signs-and-network-structure-theory-and-application
s-revay/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Modeling the Co-Evolution of Cultu
re\, Signs and Network Structure: Theory and Applications

\n

Friday\, October 20\,3:00 p.m.\nCenter f
or Social Complexity Suite\n3rd Floor\, Research Hall

\n

ABSTRA
CT: I focus on the drivers of diffusion and adoption of
cultural traits\, such as values\, beliefs\, and behaviors. I adopt an evo
lutionary view of cultural dynamics. I use concepts from dual-inheritance
theories of cultural evolution to develop and test an agent-based model ca
pable of simulating the changing distributions of cultural traits in a lar
ge population of actors over the course of prolonged periods of time. Part
icularly\, I pay close attention to the mechanisms of indirectly biased tr
ansmission of traits and guided variation\, which are both hypothesized to
be significant aspects of cultural dynamics. Indirectly biased transmissi
on consists of the adoption of specific trait variants on the basis of pos
session of initially unrelated external markers. Guided variation is then
individual adaptation driven by self-exploration.

\n

Furthermore\, I make use of large publicly available da
tasets to validate my models. The first one of these is the database of bi
ll co-authorship in the U.S. House of Representatives from 1973 to 2008. T
he other is a comprehensive dataset of scientific co-authorship in various
disciplines stretching back for over a century.

\n

The results show that cultural evolution models based on
indirectly biased transmission and guided variation are suitable to expla
ining the dynamics of various complex social networks.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3982@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 703-993-5017\; jmarr2@masonlive.gmu.edu\; https://cos
.gmu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR \nBartley Richa
rdson\, PhD\nSotera Defense Solutions\nRepresentation of Cyber Knowledge a
s Discrete Sequences\nMonday\, October 23\, 4:30-5:45\nExploratory Hall\,
Room 3301\n \nAbstract: As devices continue to gain network connectivity
and require less interactivity from human operators\, the nature of networ
k transmissions is shifting and data is being created faster and with more
fidelity than ever before. One way to view this data is in the context of
a cyber language\, analogous but semantically/syntactically different tha
n a natural language. After sequences are constructed over a large dataset
(PB)\, unsupervised machine learning and deep learning techniques are use
d to model communication\, identifying typical behavior and flagging unlik
ely events. This seminar presents context for the foundations of this new
approach to cyber anomaly detection as well as the enabling analytic techn
iques.\nDr. Richardson has nearly a decade of experience in Data Science\,
Cloud Computing\, Software Development\, and Machine Learning. He has ser
ved as both Department Chair and Visiting Professor at two universities an
d has published over 10 articles. He is currently serving as a principal d
ata scientist\, technical lead\, and principal investigator on multiple go
vernment sponsored projects\, including one DARPA research program.\n(RICH
ARDSON) 20171023_CyberLanguageGMU_v2
DTSTART;TZID=America/New_York:20171023T163000
DTEND;TZID=America/New_York:20171023T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Representation of
Cyber Knowledge as Discrete Sequences – Richardson
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-representation-of-cyber-knowledge-as-discrete-sequences-richardson/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
25/2017/08/RICHARDSON-300x200.jpg\;251\;167\,medium\;http://cos.gmu.edu/cd
s/wp-content/uploads/sites/25/2017/08/RICHARDSON-300x200.jpg\;251\;167\,la
rge\;http://cos.gmu.edu/cds/wp-content/uploads/sites/25/2017/08/RICHARDSON
-300x200.jpg\;251\;167\,full\;http://cos.gmu.edu/cds/wp-content/uploads/si
tes/25/2017/08/RICHARDSON-300x200.jpg\;251\;167
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

\n

COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR <
/em>

\n

Bartley Richardson\, PhD\nSo
tera Defense Solutions

\n

Represen
tation of Cyber Knowledge as Discrete Sequences

\n

Monday\, October 23\, 4:30-5:45\nExploratory Hall
\, Room 3301

\n

\n

Abstract: As devices continue to gain netwo
rk connectivity and require less interactivity from human operators\, the
nature of network transmissions is shifting and data is being created fast
er and with more fidelity than ever before. One way to view this data is i
n the context of a cyber language\, analogous but semantically/syntactical
ly different than a natural language. After sequences are constructed over
a large dataset (PB)\, unsupervised machine learning and deep learning te
chniques are used to model communication\, identifying typical behavior an
d flagging unlikely events. This seminar presents context for the foundati
ons of this new approach to cyber anomaly detection as well as the enablin
g analytic techniques.

\n

Dr. Richardson has nearly a decade of exper
ience in Data Science\, Cloud Computing\, Software Development\, and Machi
ne Learning. He has served as both Department Chair and Visiting Professor
at two universities and has published over 10 articles. He is currently s
erving as a principal data scientist\, technical lead\, and principal inve
stigator on multiple government sponsored projects\, including one DARPA r
esearch program.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4299@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 7039935017\; jmarr2@masonlive.gmu.edu\; https://cos.g
mu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR\n\n\nRobert Axt
ell\, PhD\nComputational Social Science Program\, Department of Computatio
nal and Data Sciences\,\nCollege of Science\nDepartment of Economics\, Col
lege of Humanities and Social Sciences\nKrasnow Institute for Advanced Stu
dy\nGeorge Mason University\nCo-Director\nComputational Public Policy Lab
\nKrasnow Institute for Advanced Study and Schar School of Policy and Gove
rnment\nExternal Professor\, Santa Fe Institute (santafe.edu)\nExternal Fa
culty\, Northwestern Institute on Complex Systems (nico.northwestern.edu)
\nScientific Advisory Council\, Waterloo Institute for Complexity and Inno
vation (wici.ca)\nAges and Lifetimes of U.S. Firms: Why Businesses Should
NOT be Treated Like People\nMonday\, October 30\, 4:30-5:45\nExploratory
Hall\, Room 3301\nABSTRACT: Over the last 150 years American corporations
have acquired many rights associated with individual citizens\, such as fr
ee speech\, the ability to make campaign contributions\, and so on. In thi
s talk I will quantify the age-related demographic properties of U.S. busi
ness firms and argue that the peculiar nature of firm aging suggests that
businesses are very much unlike individual people. Specifically\, using da
ta on all 6 million U.S. firms having employees\, I document that firm age
s are discrete Weibull-distributed while firm lifetimes follow a closely-r
elated distribution. Further\, the hazard rates associated with firm survi
val are monotone declining according to a power law. From this the expecte
d remaining lifetime can be computed and it will be demonstrated that this
INCREASES as firms age. Specifically\, while a new firm in the U.S. can e
xpect to live for about 15 years\, a firm that has survived 50 years can e
xpect to live for 30 more. Finally\, conditioning on firm size leads to ev
en more extreme results: increasing firm size by a decade cuts the hazard
rate in half. In sum these results suggest that firm aging is very differe
nt from biological aging and makes analogies between firms and people both
quantitatively inaccurate and qualitatively wrong-headed. Technically\, t
his talk will focus on the application of conventional demographic techniq
ues to economic and financial data\, including failure/survival analysis w
ith censored data.\nRob Axtell earned an interdisciplinary Ph.D. degree at
Carnegie Mellon University\, where he studied computing\, social science\
, and public policy. His teaching and research involves computational and
mathematical modeling of social and economic processes. Specifically\, he
works at the intersection of multi-agent systems computer science and the
social sciences\, building so-called agent-based models for a variety of m
arket and non-market phenomena.\nHis research has been published in the le
ading scientific journals\, including Science and the Proceedings of the N
ational Academy of Sciences\, USA\, and reprised in Nature\, and has appea
red in top disciplinary journals (e.g.\, American Economic Review\, Comput
ational and Mathematical Organization Theory\, Economic Journal)\, in gene
ral interest journals (e.g.\, PLOS One) and in specialty journals (e.g.\,
Journal of Regulatory Economics\, Technology Forecasting and Social Change
.) His research has been supported by American philanthropies (e.g.\, John
D. and Catherine T. MacArthur Foundation\, Institute for New Economic Thi
nking) and government organizations (e.g.\, National Science Foundation\,
Department of Defense\, Small Business Administration\, Office of Naval Re
search\, Environmental Protection Agency). Stories about his research have
appeared in major magazines (e.g.\, Economist\, Atlantic Monthly\, Scient
ific American\, New Yorker\, Discover\, Wired\, New Scientist\, Technology
Review\, Forbes\, Harvard Business Review\, Science News\, Chronicle of H
igher Education\, Byte\, Le Temps Strategique) and newspapers (e.g.\, Wall
St. Journal\, Washington Post\, Los Angeles Times\, Boston Globe\, Detroi
t Free Press\, Financial Times). He is co-author of Growing Artificial Soc
ieties: Social Science from the Bottom Up (MIT Press) with J.M. Epstein\,
widely cited as an example of how to apply modern computing to the analysi
s of social and economic phenomena.
DTSTART;TZID=America/New_York:20171030T163000
DTEND;TZID=America/New_York:20171030T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Ages and Lifetime
s of U.S. Firms: Why Businesses Should NOT be Treated Like People – Axell
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-ages-and-lifetimes-of-us-firms-why-businesses-should-not-be-treated-
like-people-axtell/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
25/2015/06/axtell.jpg\;166\;188\,medium\;http://cos.gmu.edu/cds/wp-content
/uploads/sites/25/2015/06/axtell.jpg\;166\;188\,large\;http://cos.gmu.edu/
cds/wp-content/uploads/sites/25/2015/06/axtell.jpg\;166\;188\,full\;http:/
/cos.gmu.edu/cds/wp-content/uploads/sites/25/2015/06/axtell.jpg\;166\;188
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

COMPUTATIONAL RESEARCH AND APPLI
CATIONS SEMINAR\n

\n

\n

Robert Axtell\, PhD\nComputational Social Science
Program\, Department of Computational and Data Sciences\,\nCollege o
f Science\nDepartment of Economics\, College of Humanities and Socia
l Sciences\nKrasnow Institute for Advanced Study\nGeorge Mason
University

\n

Co-Director\nComputat
ional Public Policy Lab\nKrasnow Institute for Advanced Study and Sc
har School of Policy and Government

Ages and Lifet
imes of U.S. Firms: Why Businesses Should NOT be Treated Like People

\n

Mon
day\, October 30\, 4:30-5:45\nExploratory Hall\, Room 3301
p>\n

ABSTRACT: Over the last 150
years American corporations have acquired many rights associated with ind
ividual citizens\, such as free speech\, the ability to make campaign cont
ributions\, and so on. In this talk I will quantify the age-related demogr
aphic properties of U.S. business firms and argue that the peculiar nature
of firm aging suggests that businesses are very much unlike individual pe
ople. Specifically\, using data on all 6 million U.S. firms having employe
es\, I document that firm ages are discrete Weibull-distributed while firm
lifetimes follow a closely-related distribution. Further\, the hazard rat
es associated with firm survival are monotone declining according to a pow
er law. From this the expected remaining lifetime can be computed and it w
ill be demonstrated that this INCREASES as firms age. Specifically\, while
a new firm in the U.S. can expect to live for about 15 years\, a firm tha
t has survived 50 years can expect to live for 30 more. Finally\, conditio
ning on firm size leads to even more extreme results: increasing firm size
by a decade cuts the hazard rate in half. In sum these results suggest th
at firm aging is very different from biological aging and makes analogies
between firms and people both quantitatively inaccurate and qualitatively
wrong-headed. Technically\, this talk will focus on the application of con
ventional demographic techniques to economic and financial data\, includin
g failure/survival analysis with censored data.

\n

Rob Axtell earned
an interdisciplinary Ph.D. degree at Carnegie Mellon University\, where he
studied computing\, social science\, and public policy. His teaching and
research involves computational and mathematical modeling of social and ec
onomic processes. Specifically\, he works at the intersection of multi-age
nt systems computer science and the social sciences\, building so-called a
gent-based models for a variety of market and non-market phenomena.

\n<
p>His research has been published in the leading scientific journals\, inc
luding Science and the Proceedings of the National Academy of
Sciences\, USA\, and reprised in Nature\, and has
appeared in top disciplinary journals (e.g.\, American Economic Review
\, Computational and Mathematical Organization Theory\,
Economic Journal)\, in general interest journals (e.g.\, PLOS One
) and in specialty journals (e.g.\, Journal of Regulatory Economi
cs\, Technology Forecasting and Social Change.) His research
has been supported by American philanthropies (e.g.\, John D. and Catheri
ne T. MacArthur Foundation\, Institute for New Economic Thinking) and gove
rnment organizations (e.g.\, National Science Foundation\, Department of D
efense\, Small Business Administration\, Office of Naval Research\, Enviro
nmental Protection Agency). Stories about his research have appeared in ma
jor magazines (e.g.\, Economist\, Atlantic Monthly\, Scientific American\, New Yorker\, Discover\,
Wired\, New Scientist\, Technology Review\, For
bes\, Harvard Business Review\, Science News\,
Chronicle of Higher Education\, Byte\, Le Temps Strategi
que) and newspapers (e.g.\, Wall St. Journal\, Washingto
n Post\, Los Angeles Times\, Boston Globe\, Det
roit Free Press\, Financial Times). He is co-author of G
rowing Artificial Societies: Social Science from the Bottom Up (MIT P
ress) with J.M. Epstein\, widely cited as an example of how to apply moder
n computing to the analysis of social and economic phenomena.\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4123@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 7039935017\; jmarr2@masonlive.gmu.edu\; https://cos.g
mu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR\nShane Frasier\
, Ph.D.\nDepartment of Homeland Security\nData Science and Cybersecurity a
t the Department of Homeland Security\nMonday\, TBA\, 4:30-5:45\nExplorat
ory Hall\, Room 3301\nABSTRACT: Among its many responsibilities\, the Depa
rtment of Homeland Security works to improve the security of the computer
networks of the federal government and our nation’s critical infrastructur
e. This will be a discussion of some of the ways in which that is done\,
and some of the ways in which data science can contribute to that goal.
DTSTART;TZID=America/New_York:20171031T163000
DTEND;TZID=America/New_York:20171031T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Data Science and
Cybersecurity at DHS – Frasier
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-tba-frasier/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

COMPUTATIONAL RESEARCH AND APPLI
CATIONS SEMINAR

\n

Shane Fra
sier\, Ph.D.\nDepartment of Homeland Security

\n

Data Science and Cybersecurity at the Department of H
omeland Security

\n

Monday\, TBA\, 4:30-5:45\nExploratory Hall\, Roo
m 3301

\n

ABSTRACT: Am
ong its many responsibilities\, the Department of Homeland Security works
to improve the security of the computer networks of the federal government
and our nation’s critical infrastructure. This will be a discussion of s
ome of the ways in which that is done\, and some of the ways in which data
science can contribute to that goal.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4301@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars\,Seminar
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\n\nNathan M. Palmer
\, Ph.D. Candidate\nComputational Social Science Program\nDepartment of Co
mputational and Data Sciences\nGeorge Mason University\nA Simple Direct Es
timate of Rule-of-Thumb Consumption using the Method of Simulated Quantile
s and Cross Validation\nFriday\, November 3\, 3:00 p.m.\nCenter for Social
Complexity Suite\n3rd Floor\, Research Hall\n \nCampbell and Mankiw (1989
\, 1990) famously demonstrated that aggregate data supported a model of ho
usehold consumption in which roughly 50% of agents followed an optimizing
strategy while the other 50% followed a “rule of thumb” strategy\, consumi
ng their current income. This paper revisits that hypothesis using structu
ral\, micro-level\, semi-parametric estimation and formally selecting betw
een different models of agent behavior. I find strong evidence supporting
a generalization of Campbell and Mankiw (1989\, 1990)’s original conclusio
n: roughly 50% of the population behaves in a way similar to “rule of thum
b” consumers\, even when the data is allowed to dictate how severe that ru
le of thumb behavior is. In addition\, this paper demonstrates the usefuln
ess and flexibility of both the Method of Simulated Quantiles and K-fold c
ross validation for selecting between of agent behavior. This type of mode
l selection is crucial for creating agents to populate robust\, richly-fea
tured agent-based models of macroprudential and macro-financial systems.\n
MSQ-Rule-of-Thumb-Consumers-GMU2017
DTSTART;TZID=America/New_York:20171103T150000
DTEND;TZID=America/New_York:20171103T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – A Simple Direct Estimate of Rule-of-
Thumb Consumption using the Method of Simulated Quantiles and Cross Valida
tion – Palmer
URL:http://cos.gmu.edu/cds/event/computational-social-science-a-simple-dire
ct-estimate-of-rule-of-thumb-consumption-using-the-method-of-simulated-qua
ntiles-and-cross-validation-palmer/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

A Simple Direct Estimate of Rule-of-Thumb Consump
tion using the Method of Simulated Quantiles and Cross Validation

\n

Friday\, November 3\, 3:00 p.m.
\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall

\n

\n

Campbell and Mankiw (1989\, 199
0) famously demonstrated that aggregate data supported a model of househol
d consumption in which roughly 50% of agents followed an optimizing strate
gy while the other 50% followed a “rule of thumb” strategy\, consuming the
ir current income. This paper revisits that hypothesis using structural\,
micro-level\, semi-parametric estimation and formally selecting between di
fferent models of agent behavior. I find strong evidence supporting a gene
ralization of Campbell and Mankiw (1989\, 1990)’s original conclusion: rou
ghly 50% of the population behaves in a way similar to “rule of thumb” con
sumers\, even when the data is allowed to dictate how severe that rule of
thumb behavior is. In addition\, this paper demonstrates the usefulness an
d flexibility of both the Method of Simulated Quantiles and K-fold cross v
alidation for selecting between of agent behavior. This type of model sele
ction is crucial for creating agents to populate robust\, richly-featured
agent-based models of macroprudential and macro-financial systems.<
/p>\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4312@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 7039935017\; jmarr2@masonlive.gmu.edu\; https://cos.g
mu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR\n\n\nJason M. K
inser\, D.Sc.\,\nChair Computational & Data Sciences\nGeorge Mason Univers
ity\nImage Operators – A World Premiere\nMonday\, November 6\, 4:30-5:45
\nExploratory Hall\, Room 3301\nABSTRACT: The onslaught of digital detecto
rs has created the ability to capture massive amounts of image data. Analy
sis techniques have been maturing for decades\, but this new flood of imag
e data will tax the foundations of information dissemination. Published de
scriptions of the image processes often consume much more real estate than
does the scripts required to execute the processes. Furthermore\, many pu
blished descriptions are imprecise. This talk will preview a new mathemati
cal language solely dedicated to the fields of image processing and analys
is. This language is coincident with implementations in Python and Matlab\
, thus there is a one-to-one correspondence between mathematical descripti
on and computer execution. This talk will present several examples and cul
minate with an interactive analysis of image processing protocols.
DTSTART;TZID=America/New_York:20171106T163000
DTEND;TZID=America/New_York:20171106T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Image Operators –
A World Premiere – Kinser
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-image-operators-a-world-premiere-kinser/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
25/2015/06/J-Kinser.jpg\;174\;201\,medium\;http://cos.gmu.edu/cds/wp-conte
nt/uploads/sites/25/2015/06/J-Kinser.jpg\;174\;201\,large\;http://cos.gmu.
edu/cds/wp-content/uploads/sites/25/2015/06/J-Kinser.jpg\;174\;201\,full\;
http://cos.gmu.edu/cds/wp-content/uploads/sites/25/2015/06/J-Kinser.jpg\;1
74\;201
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

ABSTRACT: The onslaught of digital detectors has
created the ability to capture massive amounts of image data. Analysis te
chniques have been maturing for decades\, but this new flood of image data
will tax the foundations of information dissemination. Published descript
ions of the image processes often consume much more real estate than does
the scripts required to execute the processes. Furthermore\, many publishe
d descriptions are imprecise. This talk will preview a new mathematical la
nguage solely dedicated to the fields of image processing and analysis. Th
is language is coincident with implementations in Python and Matlab\, thus
there is a one-to-one correspondence between mathematical description and
computer execution. This talk will present several examples and culminate
with an interactive analysis of image processing protocols.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3975@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 703-993-5017\; jmarr2@masonlive.gmu.edu\; https://cos
.gmu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR \nBenjamin J. R
adford\, PhD\nPrincipal Data Scientist\nSotera Defense Solutions\nClusteri
ng Techniques for Unsupervised Machine Learning\nMonday\, November 13\, 4
:30-5:45\nExploratory Hall\, Room 3301\n \nAbstract: Cluster analysis repr
esents a broad class of unsupervised algorithms that are applicable to a v
ariety of data science problems. An overview of some clustering models is
provided and example use cases for clustering are discussed. Multivariate
Gaussian mixture models are then discussed in detail and estimation techni
ques are outlined. K-selection is also discussed in the context of Gaussia
n mixture models. The talk concludes with a short discussion about how clu
stering techniques might be used in the context of cybersecurity.\nDr. Rad
ford is a Principal Data Scientist at Sotera Defense Solutions where he wo
rks on data-driven cybersecurity research programs for the Department of D
efense. He received his Ph.D. in political science from Duke University in
2016. His research interests include political methodology\, security and
political conflict\, the political implications of cyberspace\, and autom
ated event data coding. Dr. Radford’s dissertation demonstrated the semi-a
utomated population of dictionaries for event-coding in novel domains. He
is also an avid guitarist.\nradford_slides_20171108
DTSTART;TZID=America/New_York:20171113T163000
DTEND;TZID=America/New_York:20171113T174500
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Clustering Techni
ques for Unsupervised Machine Learning – Radford
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-tba-radford/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://cos.gmu.edu/cds/wp-content/uploads/sites/
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tent/uploads/sites/25/2017/08/benradford.png\;256\;256\,large\;http://cos.
gmu.edu/cds/wp-content/uploads/sites/25/2017/08/benradford.png\;256\;256\,
full\;http://cos.gmu.edu/cds/wp-content/uploads/sites/25/2017/08/benradfor
d.png\;256\;256
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Abstract: Cluster anal
ysis represents a broad class of unsupervised algorithms that are applicab
le to a variety of data science problems. An overview of some clustering m
odels is provided and example use cases for clustering are discussed. Mult
ivariate Gaussian mixture models are then discussed in detail and estimati
on techniques are outlined. K-selection is also discussed in the context o
f Gaussian mixture models. The talk concludes with a short discussion abou
t how clustering techniques might be used in the context of cybersecurity.

\n

Dr. Radford is a Principal Data Scientist at Sotera Defense Solut
ions where he works on data-driven cybersecurity research programs for the
Department of Defense. He received his Ph.D. in political science from Du
ke University in 2016. His research interests include political methodolog
y\, security and political conflict\, the political implications of cybers
pace\, and automated event data coding. Dr. Radford’s dissertation demonst
rated the semi-automated population of dictionaries for event-coding in no
vel domains. He is also an avid guitarist.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4349@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars\,Seminar
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nSally Evans\, Coor
dinator\nUniversity Dissertation and Thesis Services\nFenwick Library\nGeo
rge Mason University\nUniversity Dissertation and Thesis Services: \n Here
to help you submit your thesis or Dissertation CORRECTLY and ON TIME\n \n
University Dissertation and Thesis Services understands that there are man
y steps in the process toward graduation and it is their goal is to make t
he process as clear\, easy\, and stress-free as possible. After Ms. Evans’
presentation\, you will have an opportunity to ask questions
DTSTART;TZID=America/New_York:20171117T150000
DTEND;TZID=America/New_York:20171117T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Here to help you submit your
thesis or Dissertation CORRECTLY and ON TIME – Evans
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-here-
to-help-you-submit-your-thesis-or-dissertation-correctly-and-on-time-evans
/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

University Dissertation and Thes
is Services: \n Here to help you submit your thesis
or Dissertation CORRECTLY and ON TIME

\n

\n

Universi
ty Dissertation and Thesis Services understands that there are many steps
in the process toward graduation and it is their goal is to make the proce
ss as clear\, easy\, and stress-free as possible. After Ms. Evans’ present
ation\, you will have an opportunity to ask questions

There will be no Computational Research and
Applications Seminar on Monday\, November 20.

\n

\n

Happy Thanksgiving!

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4117@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:Computational Research and Applications Seminars
CONTACT:Joseph Marr\; 703-993-5017\; jmarr2@masonlive.gmu.edu\; https://cos
.gmu.edu/cds/
DESCRIPTION:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR\n\nJames Glasbr
enner\, PhD\nAssistant Professor\nGeorge Mason University\nReproducible Re
search & Best Practices for Computational Science\nMonday\, December 4\, 4
:30-5:45\nExploratory Hall\, Room 3301\nABSTRACT: Have you ever had one o
f the following thoughts while working on your research?\n\nI can’t rememb
er where I put that data file.\nI knew what these variables meant when I w
rote them last year.\nDid I accidentally delete that email with the final
version of our research paper attached?\nWhy does my collaborator’s progra
m delete the last row and column of this array before entering the main lo
op?\n\nIf so\, then you’re not alone\, because “most researchers are never
taught the equivalent of basic lab skills for research computing” [1]. Th
is situation persists even as the average scientific researcher devotes as
much as 30% of their time developing and 40% of their time using scientif
ic software [2]. Underdeveloped skills in programming\, project organizati
on\, and documentation can lead to general frustration\, productivity loss
es\, an increase in the risk that a researcher won’t be able to reproduce
his or her work\, and can even result in serious computational errors that
invalidate a study’s general conclusions [3]. At the same time\, the numb
er of scientific research groups that are integrating data science topics
and methods into their programs is increasing at a rapid pace1 \, further
increasing the overall need to address this disparity. In response\, a gro
wing movement of researchers has emerged that are interested in tackling t
his problem\, leading to the creation of organizations like the Software C
arpentry Foundation [4]\, guidelines for reproducible research [5]\, and s
uggestions of “best practices” for scientific computing [1\, 6\, 7]. Howev
er\, although there is more awareness about these potential solutions than
in past years\, these ideas are still not common knowledge. In this semin
ar\, I will review the general background behind these ideas and what comp
utational researchers can learn from other fields such as the software ind
ustry. Drawing on my own experience with implementing these ideas\, I will
provide examples of how you can integrate reproducible research ideas int
o your work using open source tools. Using the “best practices” suggestion
s as a guide\, I will also show ways in which you can better organize your
projects and some ways to make your code more readable\, and then explain
how this can help streamline scientific collaboration. Finally\, I will c
lose by reflecting on the role that automation can play in achieving these
principles and goals.\nReferences\n[1] G. Wilson\, J. Bryan\, K. Cranston
\, J. Kitzes\, L. Nederbragt\, and T. K. Teal\, PLoS Comput. Biol. 13\, e1
005510 (2017).\n[2] J. E. Hannay\, C. MacLeod\, J. Singer\, H. P. Langtang
en\, D. Pfahl\, and G. Wilson\, in Proc. 2009 31st Int. Conf. Softw. Eng.
ICSE Workshops (2009) pp. 1–8.\n[3] Z. Merali\, Nature 467\, 775 (2010).\n
[4] “Software Carpentry\,” .\n[5] R. D. Peng\, Science 334\, 1226 (2011).
\n[6] G. Wilson\, D. A. Aruliah\, C. T. Brown\, N. P. C. Hong\, M. Davis\,
R. T. Guy\, S. H. D. Haddock\, K. D. Huff\, I. M. Mitchell\, M. D. Plumbl
ey\, B. Waugh\, E. P. White\, and P. Wilson\, PLoS Biol. 12\, e1001745 (20
14).\n[7] V. Stodden and S. Miguez\, J. Open Res. Softw. 2\, e21 (2014). 1
An arXiv query for all pre-prints with metadata containing the term ”data
science” reveals exponential growth\, with the number of submissions appro
ximately doubling every year since 2007.
DTSTART;TZID=America/New_York:20171204T163000
DTEND;TZID=America/New_York:20171204T180000
LOCATION:Exploratory Hall\, Room 3301\, Fairfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Reproducible Rese
arch & Best Practices for Computational Science – Glasbrenner
URL:http://cos.gmu.edu/cds/event/computational-research-and-applications-se
minar-reproducible-research-and-best-practices-for-comp-science-glasbrenne
r/
X-COST-TYPE:free
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25/2016/04/2016Fall_jkglasbrenner_headshot_resized_square-300x300.jpg\;244
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e\;http://cos.gmu.edu/cds/wp-content/uploads/sites/25/2016/04/2016Fall_jkg
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.gmu.edu/cds/wp-content/uploads/sites/25/2016/04/2016Fall_jkglasbrenner_he
adshot_resized_square-300x300.jpg\;244\;244
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

ABSTRACT: Have you ever had o
ne of the following thoughts while working on your research?

\n

\nI can’t remember where I put that data file.\n

I kn
ew what these variables meant when I wrote them last year.

\n

Did I accidentally delete that email with the final version of our res
earch paper attached?

\n

Why does my collaborator’s progra
m delete the last row and column of this array before entering the main lo
op?

\n

\n

If so\, then you’re not alone\, because “most res
earchers are never taught the equivalent of basic lab skills for research
computing” [1]. This situation persists even as the average sci
entific researcher devotes as much as 30% of their time developing and 40%
of their time using scientific software [2]. Underdeveloped sk
ills in programming\, project organization\, and documentation can lead to
general frustration\, productivity losses\, an increase in the risk that
a researcher won’t be able to reproduce his or her work\, and can even res
ult in serious computational errors that invalidate a study’s general conc
lusions [3]. At the same time\, the number of scientific resear
ch groups that are integrating data science topics and methods into their
programs is increasing at a rapid pace1 \, further increasing the overall
need to address this disparity. In response\, a growing movement of resear
chers has emerged that are interested in tackling this problem\, leading t
o the creation of organizations like the Software Carpentry Foundation [4]\, guidelines for reproducible research [5]\, and suggestions o
f “best practices” for scientific computing [1\, 6\, 7]. Howeve
r\, although there is more awareness about these potential solutions than
in past years\, these ideas are still not common knowledge. In this semina
r\, I will review the general background behind these ideas and what compu
tational researchers can learn from other fields such as the software indu
stry. Drawing on my own experience with implementing these ideas\, I will
provide examples of how you can integrate reproducible research ideas into
your work using open source tools. Using the “best practices” suggestions
as a guide\, I will also show ways in which you can better organize your
projects and some ways to make your code more readable\, and then explain
how this can help streamline scientific collaboration. Finally\, I will cl
ose by reflecting on the role that automation can play in achieving these
principles and goals.

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4317@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars\,Seminar
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nWilliam B. Rouse\,
Ph.D.\nAlexander Crombie Humphreys Chair\nSchool of Systems and Enterpris
es\nStevens Institute of Technology\nComputational Social Science at Sever
al Levels\nFriday\, December 8\, 3:00 p.m.\nCenter for Social Complexity S
uite\n3rd Floor\, Research Hall\nABSTRACT: Computational social science ca
n enable understanding – and design – of a wide variety of phenomena at an
enormous range of levels. This lecture will address processes\, organizat
ions\, ecosystems\, and society for phenomena associated with disease and
medicine\, health and well being\, and technology adoption\, particularly
in automobiles. The process level is addressed in terms of scaling and opt
imization of medical innovations. The level of organizations is considered
in the context of health provider corporations’ responses to the Affordab
le Care Act. The ecosystem level is discussed in addressing population hea
lth – integrated delivery of health\, education\, and social services – in
the highly fragmented US ecosystem. The society level is considered in te
rms of the expected disruptive impacts of driverless cars on automotive\,
insurance\, and finance industries. Approaches to and challenges of modeli
ng at these differing levels are discussed.
DTSTART;TZID=America/New_York:20171208T150000
DTEND;TZID=America/New_York:20171208T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE – Computational Social Science At Seve
ral Levels – Rouse
URL:http://cos.gmu.edu/cds/event/computational-social-science-computational
-social-science-at-several-levels-rouse/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Friday\, December 8\, 3:00 p.
m.\nCenter for Social Complexity Suite\n3rd Floor\, Research H
all

\n

ABSTRACT: Computational social science can enable understandin
g – and design – of a wide variety of phenomena at an enormous range of le
vels. This lecture will address processes\, organizations\, ecosystems\, a
nd society for phenomena associated with disease and medicine\, health and
well being\, and technology adoption\, particularly in automobiles. The p
rocess level is addressed in terms of scaling and optimization of medical
innovations. The level of organizations is considered in the context of he
alth provider corporations’ responses to the Affordable Care Act. The ecos
ystem level is discussed in addressing population health – integrated deli
very of health\, education\, and social services – in the highly fragmente
d US ecosystem. The society level is considered in terms of the expected d
isruptive impacts of driverless cars on automotive\, insurance\, and finan
ce industries. Approaches to and challenges of modeling at these differing
levels are discussed.

\n

END:VEVENT
BEGIN:VEVENT
UID:ai1ec-4441@cos.gmu.edu/cds
DTSTAMP:20171214T023244Z
CATEGORIES;LANGUAGE=en-US:CSS Friday Seminars\,Seminar
CONTACT:Karen Underwood\; 7039939298\; kunderwo@gmu.edu\; https://cos.gmu.e
du/cds/
DESCRIPTION:COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR\nNiloofar Jebelli\,
MAIS-CSS Student\nGeorge Mason University\nUrban Development Through the
Lens of Agent-Based Modeling\nFriday\, December 15\, 3:00 p.m.\nCenter for
Social Complexity Suite\n3rd Floor\, Research Hall\nAbstract: Cities ar
e ever changing and growing phenomenon with many underlying complexities.
Through its life cycle\, a city experiences various forms of dynamics. Mod
els allow for a better understanding of such complexities and dynamics. Th
e model presented in this talk simulates the dynamics of certain processes
such as: an urban market\, agent interactions in that market\, urban grow
th\, sprawl and shrinkage and gentrification. The purpose of this model is
to understand the behavioral pattern of the agents and demonstrate the li
fe cycle of a city based on individual agents’ actions. This model is sign
ificant in its integration of various subsystems creating a larger system
while observing developers’ behavior. Specifically\, the model explores so
me well-known issues\, including the Smith’s rent-gap theory\, Burgess’s c
oncentric zones model of urban growth\, and Alonso’s bid rent theory. The
main results from the model show that the agents move to and reside in pro
perties within their income range\, with similar neighbors. This is one of
the first models that provides a new lens to explore urban development.
DTSTART;TZID=America/New_York:20171215T150000
DTEND;TZID=America/New_York:20171215T163000
LOCATION:Center for Social Complexity Suite 3rd Floor\, Research Hall\, Fai
rfax Campus
SEQUENCE:0
SUMMARY:COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Urban Development Through th
e Lens of Agent-Based Modeling – Jebelli
URL:http://cos.gmu.edu/cds/event/computational-social-science-seminar-urban
-development-through-the-lens-of-agent-based-modeling-jebelli/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n

Abstract: Cities are ever changing
and growing phenomenon with many underlying complexities. Through its life
cycle\, a city experiences various forms of dynamics. Models allow for a
better understanding of such complexities and dynamics. The model presente
d in this talk simulates the dynamics of certain processes such as: an urb
an market\, agent interactions in that market\, urban growth\, sprawl and
shrinkage and gentrification. The purpose of this model is to understand t
he behavioral pattern of the agents and demonstrate the life cycle of a ci
ty based on individual agents’ actions. This model is significant in its i
ntegration of various subsystems creating a larger system while observing
developers’ behavior. Specifically\, the model explores some well-known is
sues\, including the Smith’s rent-gap theory\, Burgess’s concentric zones
model of urban growth\, and Alonso’s bid rent theory. The main results fro
m the model show that the agents move to and reside in properties within t
heir income range\, with similar neighbors. This is one of the first model
s that provides a new lens to explore urban development.